135 results on '"Ding, Zhengming"'
Search Results
2. Extreme Low-Resolution Action Recognition with Confident Spatial-Temporal Attention Transfer
- Author
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Bai, Yucai, Zou, Qin, Chen, Xieyuanli, Li, Lingxi, Ding, Zhengming, and Chen, Long
- Published
- 2023
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- View/download PDF
3. CLCLSA: Cross-omics linked embedding with contrastive learning and self attention for integration with incomplete multi-omics data
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Zhao, Chen, Liu, Anqi, Zhang, Xiao, Cao, Xuewei, Ding, Zhengming, Sha, Qiuying, Shen, Hui, Deng, Hong-Wen, and Zhou, Weihua
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- 2024
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4. A zero-shot learning framework via cluster-prototype matching
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Zhang, Jing, Li, Qingyong, Geng, YangLi-ao, Wang, Wen, Sun, Wenju, Shi, Chuan, and Ding, Zhengming
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- 2022
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5. Improving Targeted Mass Spectrometry Data Analysis with Nested Active Machine Learning.
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Bao, Duran, Shu, Qingbo, Ning, Bo, Tang, Michael, Liu, Yubing, Wong, Noel, Ding, Zhengming, Zheng, Zizhan, Lyon, Christopher J., Hu, Tony, and Fan, Jia
- Subjects
MACHINE learning ,LIQUID chromatography-mass spectrometry - Abstract
Targeted mass spectrometry (MS) holds promise for precise protein and protein‐representative peptide identification and quantification, enhancing disease diagnosis. However, its clinical application is hindered by complex data analysis and expert review requirements. It is hypothesized that machine learning (ML) models can automate data analysis to accelerate the clinical application of MS. The approach involves an ML‐driven pipeline that extracts statistical and morphological features from an MS target region and feeds these features into ML algorithms to generate and assess predictive models. The findings demonstrate ML prediction models exhibit superior performance when trained on extracted features versus raw spectra intensity data and that random forest models exhibit robust classification performance in both internal and external validation datasets. These models remain effective across varying training dataset sizes and positive sample rates and are enhanced by a nested active learning approach. This approach can thus revolutionize clinical MS applications. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
6. Sparsely-labeled source assisted domain adaptation
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Wang, Wei, Chen, Shenglun, Xiang, Yuankai, Sun, Jing, Li, Haojie, Wang, Zhihui, Sun, Fuming, Ding, Zhengming, and Li, Baopu
- Published
- 2021
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7. MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification
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Wang, Tongxin, Shao, Wei, Huang, Zhi, Tang, Haixu, Zhang, Jie, Ding, Zhengming, and Huang, Kun
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- 2021
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8. Can Domain Adaptation Improve Accuracy and Fairness of Skin Lesion Classification?
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Wang, Janet, Zhang, Yunbei, Ding, Zhengming, and Hamm, Jihun
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FOS: Computer and information sciences ,Computer Science - Computers and Society ,Computer Science - Machine Learning ,Computer Vision and Pattern Recognition (cs.CV) ,Computers and Society (cs.CY) ,Computer Science - Computer Vision and Pattern Recognition ,Machine Learning (cs.LG) - Abstract
Deep learning-based diagnostic system has demonstrated potential in classifying skin cancer conditions when labeled training example are abundant. However, skin lesion analysis often suffers from a scarcity of labeled data, hindering the development of an accurate and reliable diagnostic system. In this work, we leverage multiple skin lesion datasets and investigate the feasibility of various unsupervised domain adaptation (UDA) methods in binary and multi-class skin lesion classification. In particular, we assess three UDA training schemes: single-, combined-, and multi-source. Our experiment results show that UDA is effective in binary classification, with further improvement being observed when imbalance is mitigated. In multi-class task, its performance is less prominent, and imbalance problem again needs to be addressed to achieve above-baseline accuracy. Through our quantitative analysis, we find that the test error of multi-class tasks is strongly correlated with label shift, and feature-level UDA methods have limitations when handling imbalanced datasets. Finally, our study reveals that UDA can effectively reduce bias against minority groups and promote fairness, even without the explicit use of fairness-focused techniques.
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- 2023
9. Visualizing Transferred Knowledge: An Interpretive Model of Unsupervised Domain Adaptation
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Xiao, Wenxiao, Ding, Zhengming, and Liu, Hongfu
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FOS: Computer and information sciences ,Computer Science - Machine Learning ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition ,Machine Learning (cs.LG) - Abstract
Many research efforts have been committed to unsupervised domain adaptation (DA) problems that transfer knowledge learned from a labeled source domain to an unlabeled target domain. Various DA methods have achieved remarkable results recently in terms of predicting ability, which implies the effectiveness of the aforementioned knowledge transferring. However, state-of-the-art methods rarely probe deeper into the transferred mechanism, leaving the true essence of such knowledge obscure. Recognizing its importance in the adaptation process, we propose an interpretive model of unsupervised domain adaptation, as the first attempt to visually unveil the mystery of transferred knowledge. Adapting the existing concept of the prototype from visual image interpretation to the DA task, our model similarly extracts shared information from the domain-invariant representations as prototype vectors. Furthermore, we extend the current prototype method with our novel prediction calibration and knowledge fidelity preservation modules, to orientate the learned prototypes to the actual transferred knowledge. By visualizing these prototypes, our method not only provides an intuitive explanation for the base model's predictions but also unveils transfer knowledge by matching the image patches with the same semantics across both source and target domains. Comprehensive experiments and in-depth explorations demonstrate the efficacy of our method in understanding the transferred mechanism and its potential in downstream tasks including model diagnosis.
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- 2023
10. Deep learning for AI-based diagnosis of skin-related neglected tropical diseases: A pilot study.
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Yotsu, Rie R., Ding, Zhengming, Hamm, Jihun, and Blanton, Ronald E.
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NEGLECTED diseases , *SCABIES , *ARTIFICIAL intelligence , *BURULI ulcer , *DEEP learning , *MACHINE learning , *CONVOLUTIONAL neural networks - Abstract
Background: Deep learning, which is a part of a broader concept of artificial intelligence (AI) and/or machine learning has achieved remarkable success in vision tasks. While there is growing interest in the use of this technology in diagnostic support for skin-related neglected tropical diseases (skin NTDs), there have been limited studies in this area and fewer focused on dark skin. In this study, we aimed to develop deep learning based AI models with clinical images we collected for five skin NTDs, namely, Buruli ulcer, leprosy, mycetoma, scabies, and yaws, to understand how diagnostic accuracy can or cannot be improved using different models and training patterns. Methodology: This study used photographs collected prospectively in Côte d'Ivoire and Ghana through our ongoing studies with use of digital health tools for clinical data documentation and for teledermatology. Our dataset included a total of 1,709 images from 506 patients. Two convolutional neural networks, ResNet-50 and VGG-16 models were adopted to examine the performance of different deep learning architectures and validate their feasibility in diagnosis of the targeted skin NTDs. Principal findings: The two models were able to correctly predict over 70% of the diagnoses, and there was a consistent performance improvement with more training samples. The ResNet-50 model performed better than the VGG-16 model. Model trained with PCR confirmed cases of Buruli ulcer yielded 1–3% increase in prediction accuracy across all diseases besides for mycetoma, over a model which training sets included unconfirmed cases. Conclusions: Our approach was to have the deep learning model distinguish between multiple pathologies simultaneously–which is close to real-world practice. The more images used for training, the more accurate the diagnosis became. The percentages of correct diagnosis increased with PCR-positive cases of Buruli ulcer. This demonstrated that it may be better to input images from the more accurately diagnosed cases in the training models also for achieving better accuracy in the generated AI models. However, the increase was marginal which may be an indication that the accuracy of clinical diagnosis alone is reliable to an extent for Buruli ulcer. Diagnostic tests also have their flaws, and they are not always reliable. One hope for AI is that it will objectively resolve this gap between diagnostic tests and clinical diagnoses with the addition of another tool. While there are still challenges to be overcome, there is a potential for AI to address the unmet needs where access to medical care is limited, like for those affected by skin NTDs. Author summary: The diagnosis of skin diseases depends in large part, though not exclusively on visual inspection. The diagnosis and management of these diseases is thus particularly amenable to teledermatology approaches. The widespread availability of cell phone technology and electronic information transfer provides new potential for access to health care in low-income countries, yet there are limited efforts targeting these neglected populations with dark skin and consequently limited availability of tools. In this study, we leveraged a collection of skin images gathered through a system of teledermatology in the West African countries of Côte d'Ivoire and Ghana, and applied deep learning, a form of artificial intelligence (AI)—to see if deep learning models can distinguish between different diseases and support their diagnosis. Skin-related neglected tropical diseases, or skin NTDs, prevail in these regions and were our target conditions: Buruli ulcer, leprosy, mycetoma, scabies, and yaws. The accuracy of prediction depended on the number of images that were fed into the model for training with marginal improvement using laboratory confirmed cases in training. Using more images and greater efforts in this area, it is possible that AI can help address the unmet needs where access to medical care is limited. [ABSTRACT FROM AUTHOR]
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- 2023
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11. RAIN: RegulArization on Input and Network for Black-Box Domain Adaptation
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Peng, Qucheng, Ding, Zhengming, Lyu, Lingjuan, Sun, Lichao, and Chen, Chen
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FOS: Computer and information sciences ,Computer Science - Machine Learning ,Artificial Intelligence (cs.AI) ,Computer Science - Artificial Intelligence ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition ,Machine Learning (cs.LG) - Abstract
Source-Free domain adaptation transits the source-trained model towards target domain without exposing the source data, trying to dispel these concerns about data privacy and security. However, this paradigm is still at risk of data leakage due to adversarial attacks on the source model. Hence, the Black-Box setting only allows to use the outputs of source model, but still suffers from overfitting on the source domain more severely due to source model's unseen weights. In this paper, we propose a novel approach named RAIN (RegulArization on Input and Network) for Black-Box domain adaptation from both input-level and network-level regularization. For the input-level, we design a new data augmentation technique as Phase MixUp, which highlights task-relevant objects in the interpolations, thus enhancing input-level regularization and class consistency for target models. For network-level, we develop a Subnetwork Distillation mechanism to transfer knowledge from the target subnetwork to the full target network via knowledge distillation, which thus alleviates overfitting on the source domain by learning diverse target representations. Extensive experiments show that our method achieves state-of-the-art performance on several cross-domain benchmarks under both single- and multi-source black-box domain adaptation., Accepted by IJCAI 2023
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- 2022
12. Locally connected graph for visual tracking
- Author
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Lu, Ke, Ding, Zhengming, and Ge, Sam
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- 2013
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13. Divergence-Agnostic Unsupervised Domain Adaptation by Adversarial Attacks.
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Li, Jingjing, Du, Zhekai, Zhu, Lei, Ding, Zhengming, Lu, Ke, and Shen, Heng Tao
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MACHINE learning ,COMMUNITIES ,KNOWLEDGE transfer ,GENERALIZATION ,FEATURE extraction - Abstract
Conventional machine learning algorithms suffer the problem that the model trained on existing data fails to generalize well to the data sampled from other distributions. To tackle this issue, unsupervised domain adaptation (UDA) transfers the knowledge learned from a well-labeled source domain to a different but related target domain where labeled data is unavailable. The majority of existing UDA methods assume that data from the source domain and the target domain are available and complete during training. Thus, the divergence between the two domains can be formulated and minimized. In this paper, we consider a more practical yet challenging UDA setting where either the source domain data or the target domain data are unknown. Conventional UDA methods would fail this setting since the domain divergence is agnostic due to the absence of the source data or the target data. Technically, we investigate UDA from a novel view—adversarial attack—and tackle the divergence-agnostic adaptive learning problem in a unified framework. Specifically, we first report the motivation of our approach by investigating the inherent relationship between UDA and adversarial attacks. Then we elaborately design adversarial examples to attack the training model and harness these adversarial examples. We argue that the generalization ability of the model would be significantly improved if it can defend against our attack, so as to improve the performance on the target domain. Theoretically, we analyze the generalization bound for our method based on domain adaptation theories. Extensive experimental results on multiple UDA benchmarks under conventional, source-absent and target-absent UDA settings verify that our method is able to achieve a favorable performance compared with previous ones. Notably, this work extends the scope of both domain adaptation and adversarial attack, and expected to inspire more ideas in the community. [ABSTRACT FROM AUTHOR]
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- 2022
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14. What and How: Generalized Lifelong Spectral Clustering via Dual Memory.
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Sun, Gan, Cong, Yang, Dong, Jiahua, Liu, Yuyang, Ding, Zhengming, and Yu, Haibin
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HIERARCHICAL clustering (Cluster analysis) ,MEMORY ,KNOWLEDGE transfer ,TASK performance ,INTUITION - Abstract
Spectral clustering (SC) has become one of the most widely-adopted clustering algorithms, and been successfully applied into various applications. We in this work explore the problem of spectral clustering in a lifelong learning framework termed as Generalized Lifelong Spectral Clustering (GL $^2$ 2 SC). Different from most current studies, which concentrate on a fixed spectral clustering task set and cannot efficiently incorporate a new clustering task, the goal of our work is to establish a generalized model for new spectral clustering tasks by “What” and “How” to lifelong learn from past tasks. In respect of “what to lifelong learn”, our GL $^2$ 2 SC framework contains a dual memory mechanism with a deep orthogonal factorization manner: an orthogonal basis memory stores hidden and hierarchical clustering centers among learned tasks, and a feature embedding memory captures deep manifold representation common across multiple related tasks. When learning a new clustering task, the intuition here for “how to lifelong learn” is that GL $^2$ 2 SC can transfer intrinsic knowledge from dual memory mechanism to obtain task-specific encoding matrix. Then the encoding matrix can redefine the dual memory over time to provide maximal benefits when learning future tasks, and reversely maximize performance for past tasks. To achieve this, we propose an alternative optimization formulation with convergence guarantee for solving our GL $^2$ 2 SC model. To the end, empirical comparisons on several benchmark datasets show the effectiveness of our GL $^2$ 2 SC, in comparison with several state-of-the-art clustering models. [ABSTRACT FROM AUTHOR]
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- 2022
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15. Continuous Multi-View Human Action Recognition.
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Wang, Qiang, Sun, Gan, Dong, Jiahua, Wang, Qianqian, and Ding, Zhengming
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HUMAN activity recognition ,COMPUTER vision ,REINFORCEMENT learning ,VISUAL fields ,ACTIVE learning - Abstract
Human action recognition which recognizes human actions in a video is a fundamental task in computer vision field. Although multiple existing methods with single-view or multi-view have been presented for human action recognition, these recognition approaches cannot be extended into new action recognition or action classification tasks, as well as discover underlying correlations among different views. To tackle the above problem, this paper proposes a new lifelong multi-view subspace learning framework for continuous human action recognition, which could exploit the complementary information amongst different views from a lifelong learning perspective. More specifically, a set of view-specific libraries is established to gradually store the useful information within multiple views. As a new action recognition task comes, we decompose the model parameters into a set of embedded parameters over view-specific libraries. A latent representation subspace is constructed via encouraging it to be close to different view-specific libraries, which can leverage the high-order correlations among different views and further avoid partial information for action recognition task. Meanwhile, we propose to employ an alternating direction strategy to optimize our proposed method. Empirical studies on real-world multi-view action recognition datasets have shown that our proposed framework attains the superior recognition performance and saves the computational time when continually learning new action recognition tasks. [ABSTRACT FROM AUTHOR]
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- 2022
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16. Joint Adaptive Dual Graph and Feature Selection for Domain Adaptation.
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Sun, Jing, Wang, Zhihui, Wang, Wei, Li, Haojie, Sun, Fuming, and Ding, Zhengming
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FEATURE selection ,MATRIX decomposition ,DISTRIBUTION (Probability theory) ,KNOWLEDGE transfer ,CONDITIONAL probability ,CHARTS, diagrams, etc. ,FEATURE extraction ,NONNEGATIVE matrices - Abstract
Domain adaptation aims to exploit domain-invariant features by aligning the cross-domain distributions in the manifold subspace for applying the classifier trained on the source domain to the target domain. However, two limitations may still deteriorate their performances: (1) the influences of noisy or irrelevant features in the original feature space are ignored, which may unexpectedly hurt the classification of target samples; (2) the graph constructed directly in the original data space cannot accurately capture the inherent local manifold structures of high-dimensional data due to the curse of dimensionality, which may seriously mislead the transferable features learning. In this paper, we propose a novel approach to address these problems, referred to as joint Adaptive Dual Graph and Feature Selection for domain adaptation (ADGFS). Specifically, feature selection can characterize the relative importance of different features through a scaling factor, which enables ADGFS to not only reduce the impacts of noisy or irrelevant features on knowledge transfer but also learn informative domain-invariant features. Meanwhile, ADGFS adaptively optimizes the dual graph by learning the similarity matrices of both instance-level and feature-level graphs in the projected low-dimensional manifold subspace rather than the original high-dimensional space, such that the intrinsic local manifold structures of data can be captured precisely. Moreover, ADGFS simultaneously aligns the marginal and conditional probability distributions in the nonnegative matrix factorization framework to narrow the distribution discrepancies between the two different domains, which can adequately transfer knowledge from the source domain to the target domain. Comprehensive experiments on four benchmark datasets can demonstrate that the effectiveness of the proposed approach in cross-domain image classification. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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17. Rethink Maximum Mean Discrepancy for Domain Adaptation
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Wang, Wei, Li, Haojie, Ding, Zhengming, and Wang, Zhihui
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FOS: Computer and information sciences ,Computer Science - Machine Learning ,Statistics - Machine Learning ,Machine Learning (stat.ML) ,Machine Learning (cs.LG) - Abstract
Existing domain adaptation methods aim to reduce the distributional difference between the source and target domains and respect their specific discriminative information, by establishing the Maximum Mean Discrepancy (MMD) and the discriminative distances. However, they usually accumulate to consider those statistics and deal with their relationships by estimating parameters blindly. This paper theoretically proves two essential facts: 1) minimizing the MMD equals to maximize the source and target intra-class distances respectively but jointly minimize their variance with some implicit weights, so that the feature discriminability degrades; 2) the relationship between the intra-class and inter-class distances is as one falls, another rises. Based on this, we propose a novel discriminative MMD. On one hand, we consider the intra-class and inter-class distances alone to remove a redundant parameter, and the revealed weights provide their approximate optimal ranges. On the other hand, we design two different strategies to boost the feature discriminability: 1) we directly impose a trade-off parameter on the implicit intra-class distance in MMD to regulate its change; 2) we impose the similar weights revealed in MMD on inter-class distance and maximize it, then a balanced factor could be introduced to quantitatively leverage the relative importance between the feature transferability and its discriminability. The experiments on several benchmark datasets not only prove the validity of theoretical results but also demonstrate that our approach could perform better than the comparative state-of-art methods substantially.
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- 2020
18. Mining Label Distribution Drift in Unsupervised Domain Adaptation
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Li, Peizhao, Ding, Zhengming, and Liu, Hongfu
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FOS: Computer and information sciences ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Unsupervised domain adaptation targets to transfer task knowledge from labeled source domain to related yet unlabeled target domain, and is catching extensive interests from academic and industrial areas. Although tremendous efforts along this direction have been made to minimize the domain divergence, unfortunately, most of existing methods only manage part of the picture by aligning feature representations from different domains. Beyond the discrepancy in feature space, the gap between known source label and unknown target label distribution, recognized as label distribution drift, is another crucial factor raising domain divergence, and has not been paid enough attention and well explored. From this point, in this paper, we first experimentally reveal how label distribution drift brings negative effects on current domain adaptation methods. Next, we propose Label distribution Matching Domain Adversarial Network (LMDAN) to handle data distribution shift and label distribution drift jointly. In LMDAN, label distribution drift problem is addressed by the proposed source samples weighting strategy, which select samples to contribute to positive adaptation and avoid negative effects brought by the mismatched in label distribution. Finally, different from general domain adaptation experiments, we modify domain adaptation datasets to create the considerable label distribution drift between source and target domain. Numerical results and empirical model analysis show that LMDAN delivers superior performance compared to other state-of-the-art domain adaptation methods under such scenarios.
- Published
- 2020
19. Sparsely-Labeled Source Assisted Domain Adaptation
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Wang, Wei, Wang, Zhihui, Xiang, Yuankai, Sun, Jing, Li, Haojie, Sun, Fuming, and Ding, Zhengming
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FOS: Computer and information sciences ,Computer Science - Machine Learning ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition ,Machine Learning (cs.LG) - Abstract
Domain Adaptation (DA) aims to generalize the classifier learned from the source domain to the target domain. Existing DA methods usually assume that rich labels could be available in the source domain. However, there are usually a large number of unlabeled data but only a few labeled data in the source domain, and how to transfer knowledge from this sparsely-labeled source domain to the target domain is still a challenge, which greatly limits their application in the wild. This paper proposes a novel Sparsely-Labeled Source Assisted Domain Adaptation (SLSA-DA) algorithm to address the challenge with limited labeled source domain samples. Specifically, due to the label scarcity problem, the projected clustering is conducted on both the source and target domains, so that the discriminative structures of data could be leveraged elegantly. Then the label propagation is adopted to propagate the labels from those limited labeled source samples to the whole unlabeled data progressively, so that the cluster labels are revealed correctly. Finally, we jointly align the marginal and conditional distributions to mitigate the cross-domain mismatch problem, and optimize those three procedures iteratively. However, it is nontrivial to incorporate those three procedures into a unified optimization framework seamlessly since some variables to be optimized are implicitly involved in their formulas, thus they could not promote to each other. Remarkably, we prove that the projected clustering and conditional distribution alignment could be reformulated as different expressions, thus the implicit variables are revealed in different optimization steps. As such, the variables related to those three quantities could be optimized in a unified optimization framework and facilitate to each other, to improve the recognition performance obviously., 22 pages, 6 figures, submitted to the Pattern Recognition
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- 2020
20. Generative Partial Multi-View Clustering
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Wang, Qianqian, Ding, Zhengming, Tao, Zhiqiang, Gao, Quanxue, and Fu, Yun
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FOS: Computer and information sciences ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Nowadays, with the rapid development of data collection sources and feature extraction methods, multi-view data are getting easy to obtain and have received increasing research attention in recent years, among which, multi-view clustering (MVC) forms a mainstream research direction and is widely used in data analysis. However, existing MVC methods mainly assume that each sample appears in all the views, without considering the incomplete view case due to data corruption, sensor failure, equipment malfunction, etc. In this study, we design and build a generative partial multi-view clustering model, named as GP-MVC, to address the incomplete multi-view problem by explicitly generating the data of missing views. The main idea of GP-MVC lies at two-fold. First, multi-view encoder networks are trained to learn common low-dimensional representations, followed by a clustering layer to capture the consistent cluster structure across multiple views. Second, view-specific generative adversarial networks are developed to generate the missing data of one view conditioning on the shared representation given by other views. These two steps could be promoted mutually, where learning common representations facilitates data imputation and the generated data could further explores the view consistency. Moreover, an weighted adaptive fusion scheme is implemented to exploit the complementary information among different views. Experimental results on four benchmark datasets are provided to show the effectiveness of the proposed GP-MVC over the state-of-the-art methods., This paper is an extension to our previous work: "Wang Q, Ding Z, Tao Z, et al. Partial multi-view clustering via consistent GAN[C]//2018 IEEE International Conference on Data Mining (ICDM). IEEE, 2018: 1290-1295."
- Published
- 2020
21. Importance Filtered Cross-Domain Adaptation
- Author
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Wang, Wei, Li, Haojie, Wang, Zhihui, Sun, Jing, Ding, Zhengming, and Sun, Fuming
- Subjects
FOS: Computer and information sciences ,Computer Science - Machine Learning ,Statistics - Machine Learning ,Machine Learning (stat.ML) ,Machine Learning (cs.LG) - Abstract
In Domain Adaptation (DA), the category-relevant losses usually occupy a dominant position, while they are usually built with hard or soft labels in existing models. We observed that hard labels are overconfident due to hard samples existed, and soft labels are ambiguous as too many small noisy probabilities involved, and both of them are easily to cause negative transfer. Besides, the category-irrelevant losses in Closed-Set DA (CSDA) paradigm fail to work in Open-Set DA (OSDA), and they also have to be in a category-relevant form, since target data samples are split into shared and private classes. To this end, we propose a newly-unified DA framework (i.e., Importance Filtered Cross-Domain Adaptation, IFCDA). Firstly, an importance filtered mechanism is devised to generate filtered soft labels to mitigate negative transfer desirably. Specifically, the soft labels are divided into confident and ambiguous ones. Then, only the maximum probability in each confident label is retained, and a threshold value is set to truncate each ambiguous label so that only prominent probabilities are reserved. Moreover, a general graph-based label propagation is contrived to attain soft labels in both CSDA and OSDA, where an extra component is embedded into label vector, so that it could detect target novel classes. Finally, the category-relevant losses in both scenarios are reformulated using filtered soft labels, while the category-irrelevant MMD loss in CSDA is reformulated as a form like class-wise MMD using newly-designed importance filtered soft labels. Notably, CSDA paradigm is a special case when all extra components are set to 0, thus the proposed approach is geared to both CSDA and OSDA. Comprehensive experiments on benchmark cross-domain object recognition datasets verify that the proposed approach outperforms several state-of-the-art methods in both scenarios., 15 pages, 7 figures, IEEE Tansactions on Image Processing. arXiv admin note: text overlap with arXiv:1906.07441 by other authors
- Published
- 2020
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22. Learning Robust Data Representation: A Knowledge Flow Perspective
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Ding, Zhengming, Shao, Ming, Zhao, Handong, and Li, Sheng
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FOS: Computer and information sciences ,Computer Science - Machine Learning ,Artificial Intelligence (cs.AI) ,Computer Science - Artificial Intelligence ,Statistics - Machine Learning ,Machine Learning (stat.ML) ,Machine Learning (cs.LG) - Abstract
It is always demanding to learn robust visual representation for various learning problems; however, this learning and maintenance process usually suffers from noise, incompleteness or knowledge domain mismatch. Thus, robust representation learning by removing noisy features or samples, complementing incomplete data, and mitigating the distribution difference becomes the key. Along this line of research, low-rank modeling has been widely-applied to solving representation learning challenges. This survey covers the topic from a knowledge flow perspective in terms of: (1) robust knowledge recovery, (2) robust knowledge transfer, and (3) robust knowledge fusion, centered around several major applications. First of all, we deliver a unified formulation for robust knowledge discovery given single dataset. Second, we discuss robust knowledge transfer and fusion given multiple datasets with different knowledge flows, followed by practical challenges, model variations, and remarks. Finally, we highlight future research of robust knowledge discovery for incomplete, unbalance, large-scale data analysis. This would benefit AI community from literature review to future direction., 7 pages, 1 figure, 1 table
- Published
- 2019
23. Maximum Density Divergence for Domain Adaptation.
- Author
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Li, Jingjing, Chen, Erpeng, Ding, Zhengming, Zhu, Lei, Lu, Ke, and Shen, Heng Tao
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DENSITY ,DATA distribution ,AUTOMATED teller machines ,LEARNING ,TASK analysis ,KNOWLEDGE transfer ,PHYSIOLOGICAL adaptation - Abstract
Unsupervised domain adaptation addresses the problem of transferring knowledge from a well-labeled source domain to an unlabeled target domain where the two domains have distinctive data distributions. Thus, the essence of domain adaptation is to mitigate the distribution divergence between the two domains. The state-of-the-art methods practice this very idea by either conducting adversarial training or minimizing a metric which defines the distribution gaps. In this paper, we propose a new domain adaptation method named adversarial tight match (ATM) which enjoys the benefits of both adversarial training and metric learning. Specifically, at first, we propose a novel distance loss, named maximum density divergence (MDD), to quantify the distribution divergence. MDD minimizes the inter-domain divergence (“match” in ATM) and maximizes the intra-class density (“tight” in ATM). Then, to address the equilibrium challenge issue in adversarial domain adaptation, we consider leveraging the proposed MDD into adversarial domain adaptation framework. At last, we tailor the proposed MDD as a practical learning loss and report our ATM. Both empirical evaluation and theoretical analysis are reported to verify the effectiveness of the proposed method. The experimental results on four benchmarks, both classical and large-scale, show that our method is able to achieve new state-of-the-art performance on most evaluations. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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- View/download PDF
24. REPEATED ETHANOL INJECTIONS INTO THE POSTERIOR VTA INCREASES THE RESPONSE OF VTA DOPAMINE NEURONS TO THE STIMULATING EFFECTS OF ETHANOL: 299
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Ding, Zhengming, McBride, William J, and Rodd, Zachary A
- Published
- 2007
25. Generative Partial Multi-View Clustering With Adaptive Fusion and Cycle Consistency.
- Author
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Wang, Qianqian, Ding, Zhengming, Tao, Zhiqiang, Gao, Quanxue, and Fu, Yun
- Subjects
- *
GENERATIVE adversarial networks , *PROBABILISTIC generative models , *GENETIC programming , *DATA corruption , *FEATURE extraction , *GALLIUM nitride - Abstract
Nowadays, with the rapid development of data collection sources and feature extraction methods, multi-view data are getting easy to obtain and have received increasing research attention in recent years, among which, multi-view clustering (MVC) forms a mainstream research direction and is widely used in data analysis. However, existing MVC methods mainly assume that each sample appears in all the views, without considering the incomplete view case due to data corruption, sensor failure, equipment malfunction, etc. In this study, we design and build a generative partial multi-view clustering model with adaptive fusion and cycle consistency, named as GP-MVC, to solve the incomplete multi-view problem by explicitly generating the data of missing views. The main idea of GP-MVC lies in two-fold. First, multi-view encoder networks are trained to learn common low-dimensional representations, followed by a clustering layer to capture the shared cluster structure across multiple views. Second, view-specific generative adversarial networks with multi-view cycle consistency are developed to generate the missing data of one view conditioning on the shared representation given by other views. These two steps could be promoted mutually, where the learned common representation facilitates data imputation and the generated data could further explores the view consistency. Moreover, an weighted adaptive fusion scheme is implemented to exploit the complementary information among different views. Experimental results on four benchmark datasets are provided to show the effectiveness of the proposed GP-MVC over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
26. Semi-Supervised Low-Rank Semantics Grouping for Zero-Shot Learning.
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Xu, Bingrong, Zeng, Zhigang, Lian, Cheng, and Ding, Zhengming
- Subjects
SUPERVISED learning ,SEMANTICS ,TASK analysis - Abstract
Zero-shot learning has received great interest in visual recognition community. It aims to classify new unobserved classes based on the model learned from observed classes. Most zero-shot learning methods require pre-provided semantic attributes as the mid-level information to discover the intrinsic relationship between observed and unobserved categories. However, it is impractical to annotate the enriched label information of the observed objects in real-world applications, which would extremely hurt the performance of zero-shot learning with limited labeled seen data. To overcome this obstacle, we develop a Low-rank Semantics Grouping (LSG) method for zero-shot learning in a semi-supervised fashion, which attempts to jointly uncover the intrinsic relationship across visual and semantic information and recover the missing label information from seen classes. Specifically, the visual-semantic encoder is utilized as projection model, low-rank semantic grouping scheme is explored to capture the intrinsic attributes correlations and a Laplacian graph is constructed from the visual features to guide the label propagation from labeled instances to unlabeled ones. Experiments have been conducted on several standard zero-shot learning benchmarks, which demonstrate the efficiency of the proposed method by comparing with state-of-the-art methods. Our model is robust to different levels of missing label settings. Also visualized results prove that the LSG can distinguish the test unseen classes more discriminative. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
27. Vehicle and Person Re-Identification With Support Neighbor Loss.
- Author
-
Li, Kai, Ding, Zhengming, Li, Kunpeng, Zhang, Yulun, and Fu, Yun
- Subjects
- *
CONVOLUTIONAL neural networks , *DEEP learning - Abstract
One of the key tasks for an intelligent visual surveillance system is to automatically re-identify objects of interest, e.g., persons or vehicles, from nonoverlapping camera views. This demand incurs the vast investigation of person re-identification (re-ID) and vehicle re-ID techniques, especially those deep learning-based ones. While most recent algorithms focus on designing new convolutional neural networks, less attention is paid to the loss functions, which are of vital roles as well. Triplet loss and softmax loss are the two losses that are extensively used, both of which, however, have limitations. Triplet loss optimizes the model to produce features with which samples from the same class have higher similarity than those from different classes. The problem of triplet loss is that the number of triplets to be constructed grows cubically with training samples, which causes scalability issue, unstable performance, and slow convergence. Softmax loss has favorable scalable property and is widely used for large-scale classification problems. However, since Softmax loss only aims to separate well training classes, its performance for re-ID tasks is not desirable because the model is tested to measure the similarity of samples from unseen classes. We propose the support neighbor (SN) loss, which avoids the limitations of the abovementioned two losses. Unlike triplet loss that is calculated based on triplets, SN loss is derived from $K$ -nearest neighbors (SNs) of anchor samples. The SNs of an anchor are unique, containing more valuable contextual information and neighborhood structure of the anchor, and thus contribute to more stable performance and reliable embedding from image space to feature space. Based on the SNs, a softmax-like separation term and a squeeze term are proposed, which encourage interclass separation and intraclass compactness, respectively. Experiments show that SN loss surpasses triplet and softmax losses with the same backbone network and reaches the state-of-the-art performance for both person and vehicle re-ID using a ResNet50 backbone when combined with training tricks. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
28. Weakly-Supervised Cross-Domain Adaptation for Endoscopic Lesions Segmentation.
- Author
-
Dong, Jiahua, Cong, Yang, Sun, Gan, Yang, Yunsheng, Xu, Xiaowei, and Ding, Zhengming
- Subjects
DIAGNOSTIC imaging ,IMAGE segmentation - Abstract
Weakly-supervised learning has attracted growing research attention on medical lesions segmentation due to significant saving in pixel-level annotation cost. However, 1) most existing methods require effective prior and constraints to explore the intrinsic lesions characterization, which only generates incorrect and rough prediction; 2) they neglect the underlying semantic dependencies among weakly-labeled target enteroscopy diseases and fully-annotated source gastroscope lesions, while forcefully utilizing untransferable dependencies leads to the negative performance. To tackle above issues, we propose a new weakly-supervised lesions transfer framework, which can not only explore transferable domain-invariant knowledge across different datasets, but also prevent the negative transfer of untransferable representations. Specifically, a Wasserstein quantified transferability framework is developed to highlight wide-range transferable contextual dependencies, while neglecting the irrelevant semantic characterizations. Moreover, a novel self-supervised pseudo label generator is designed to equally provide confident pseudo pixel labels for both hard-to-transfer and easy-to-transfer target samples. It inhibits the enormous deviation of false pseudo pixel labels under the self-supervision manner. Afterwards, dynamically-searched feature centroids are aligned to narrow category-wise distribution shift. Comprehensive theoretical analysis and experiments show the superiority of our model on the endoscopic dataset and several public datasets. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
29. Heterogeneous Recommendation via Deep Low-Rank Sparse Collective Factorization.
- Author
-
Jiang, Shuhui, Ding, Zhengming, and Fu, Yun
- Subjects
- *
PROCESS optimization , *LIKES & dislikes , *KNOWLEDGE transfer , *SPARSE matrices , *STOCHASTIC processes - Abstract
A real-world recommender usually adopts heterogeneous types of user feedbacks, for example, numerical ratings such as 5-star grades and binary ratings such as likes and dislikes. In this work, we focus on transferring knowledge from binary ratings to numerical ratings, facing a more serious data sparsity problem. Conventional Collective Factorization methods usually assume that there are shared user and item latent factors across multiple related domains, but may ignore the shared common knowledge of rating patterns. Furthermore, existing works may also fail to consider the hierarchical structures in the heterogeneous recommendation scenario (i.e., genre, sub-genre, detailed-category). To address these challenges, in this paper, we propose a novel Deep Low-rank Sparse Collective Factorization (DLSCF) framework for heterogeneous recommendation. Specifically, we adopt low-rank sparse decomposition to capture the common rating patterns in related domains while splitting the domain-specific patterns. We also factorize the model in multiple layers to capture the affiliation relation between latent categories and sub-categories. We propose both batch and Stochastic Gradient Descent (SGD) based optimization algorithms for solving DLSCF. Experimental results on MoviePilot, Netfilx, Flixter, MovieLens10M and MovieLens20M datasets demonstrate the effectiveness of the proposed algorithms, by comparing them with several state-of-the-art batch and SGD based approaches. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
30. Generative Zero-Shot Learning via Low-Rank Embedded Semantic Dictionary.
- Author
-
Ding, Zhengming, Shao, Ming, and Fu, Yun
- Subjects
- *
VISUAL learning , *GENERALIZABILITY theory , *LATENT class analysis (Statistics) , *GALLIUM nitride - Abstract
Zero-shot learning for visual recognition, which approaches identifying unseen categories through a shared visual-semantic function learned on the seen categories and is expected to well adapt to unseen categories, has received considerable research attention most recently. However, the semantic gap between discriminant visual features and their underlying semantics is still the biggest obstacle, because there usually exists domain disparity across the seen and unseen classes. To deal with this challenge, we design two-stage generative adversarial networks to enhance the generalizability of semantic dictionary through low-rank embedding for zero-shot learning. In detail, we formulate a novel framework to simultaneously seek a two-stage generative model and a semantic dictionary to connect visual features with their semantics under a low-rank embedding. Our first-stage generative model is able to augment more semantic features for the unseen classes, which are then used to generate more discriminant visual features in the second stage, to expand the seen visual feature space. Therefore, we will be able to seek a better semantic dictionary to constitute the latent basis for the unseen classes based on the augmented semantic and visual data. Finally, our approach could capture a variety of visual characteristics from seen classes that are “ready-to-use” for new classes. Extensive experiments on four zero-shot benchmarks demonstrate that our proposed algorithm outperforms the state-of-the-art zero-shot algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
31. Cross-Generation Kinship Verification with Sparse Discriminative Metric.
- Author
-
Wang, Shuyang, Ding, Zhengming, and Fu, Yun
- Subjects
- *
KINSHIP , *FATHER-son relationship , *GENERATION gap , *FORENSIC sciences , *MISSING persons - Abstract
Kinship verification is a very important technique in many real-world applications, e.g., personal album organization, missing person investigation and forensic analysis. However, it is extremely difficult to verify a family pair with generation gap, e.g., father and son, since there exist both age gap and identity variation. It is essential to well fight off such challenges to achieve promising kinship verification performance. To this end, we propose a towards-young cross-generation model for effective kinship verification by mitigating both age and identity divergences. Specifically, we explore a conditional generative model to bring in an intermediate domain to bridge each pair. Thus, we could extract more effective features through deep architectures with a newly-designed Sparse Discriminative Metric Loss (SDM-Loss), which is exploited to involve the positive and negative information. Experimental results on kinship benchmark demonstrate the superiority of our proposed model by comparing with the state-of-the-art kinship verification methods. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
32. Robust Discriminative Metric Learning for Image Representation.
- Author
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Ding, Zhengming, Shao, Ming, Hwang, Wonjun, Suh, Sungjoov, Han, Jae-Joon, Choi, Changkyu, and Fu, Yun
- Subjects
- *
IMAGE representation , *HUMAN facial recognition software , *SUPERVISED learning , *DATA structures , *MACHINE learning , *DEEP learning - Abstract
Metric learning has attracted significant attention in the past decades, because of its appealing advances in various real-world tasks, e.g., person re-identification and face recognition. Traditional supervised metric learning attempts to seek a discriminative metric, which could minimize the pairwise distance of within-class data samples, while maximizing the pairwise distance of data samples from various classes. However, it is still a challenge to build a robust and discriminative metric, especially for corrupted data in the real-world application. In this paper, we propose a Robust Discriminative Metric Learning algorithm through fast low-rank representation and denoising strategy. To be specific, the metric learning problem is guided by a discriminative regularization by incorporating the pair-wise or class-wise information. Moreover, the low-rank basis learning is jointly optimized with the metric to better uncover the global data structure and remove noise. Furthermore, the fast low-rank representation is implemented to mitigate the computational burden and ensure the scalability on large-scale datasets. Finally, we evaluate our learned metric on several challenging tasks, e.g., face recognition/verification, object recognition, image clustering, and person re-identification. The experimental results verify the effectiveness of our proposed algorithm in comparison to many metric learning algorithms, even deep learning ones. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
33. Discriminative Transfer Feature and Label Consistency for Cross-Domain Image Classification.
- Author
-
Li, Shuang, Liu, Chi Harold, Su, Limin, Xie, Binhui, Ding, Zhengming, Chen, C. L. Philip, and Wu, Dapeng
- Subjects
LABELS ,VISUAL accommodation ,IMAGE representation ,DATA distribution - Abstract
Visual domain adaptation aims to seek an effective transferable model for unlabeled target images by benefiting from the well-labeled source images following different distributions. Many recent efforts focus on extracting domain-invariant image representations via exploring target pseudo labels, predicted by the source classifier, to further mitigate the conditional distribution shift across domains. However, two essential factors are overlooked by most existing methods: 1) the learned transferable features should be not only domain invariant but also category discriminative; and 2) the target pseudo label is a two-edged sword to cross-domain alignment. In other words, the wrongly predicted target labels may hinder the class-wise domain matching. In this article, to address these two issues simultaneously, we propose a discriminative transfer feature and label consistency (DTLC) approach for visual domain adaptation problems, which can naturally unify cross-domain alignment with discriminative information preserved and label consistency of source and target data into one framework. To be specific, DTLC first incorporates class discriminative information by penalizing the maximum distance of data pair in the same class and the minimum distance of data pair sharing the different labels for each data into the distribution alignment of both domains. The target pseudo labels are then refined based on the label consistency within the domains. Thus, the transfer feature learning and coarse-to-fine target labels would be coupled to benefit each other in an iterative way. Comprehensive experiments on several visual cross-domain benchmarks verify that DTLC can gain remarkable margins over state-of-the-art (SOTA) nondeep visual domain adaptation methods and even be comparable to competitive deep domain adaptation ones. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
34. Marginalized Multiview Ensemble Clustering.
- Author
-
Tao, Zhiqiang, Liu, Hongfu, Li, Sheng, Ding, Zhengming, and Fu, Yun
- Subjects
INFORMATION commons ,DATA corruption - Abstract
Multiview clustering (MVC), which aims to explore the underlying cluster structure shared by multiview data, has drawn more research efforts in recent years. To exploit the complementary information among multiple views, existing methods mainly learn a common latent subspace or develop a certain loss across different views, while ignoring the higher level information such as basic partitions (BPs) generated by the single-view clustering algorithm. In light of this, we propose a novel marginalized multiview ensemble clustering (M2VEC) method in this paper. Specifically, we solve MVC in an EC way, which generates BPs for each view individually and seeks for a consensus one. By this means, we naturally leverage the complementary information of multiview data upon the same partition space. In order to boost the robustness of our approach, the marginalized denoising process is adopted to mimic the data corruptions and noises, which provides robust partition-level representations for each view by training a single-layer autoencoder. A low-rank and sparse decomposition is seamlessly incorporated into the denoising process to explicitly capture the consistency information and meanwhile compensate the distinctness between heterogeneous features. Spectral consensus graph partitioning is also involved by our model to make M2VEC as a unified optimization framework. Moreover, a multilayer M2VEC is eventually delivered in a stacked fashion to encapsulate nonlinearity into partition-level representations for handling complex data. Experimental results on eight real-world data sets show the efficacy of our approach compared with several state-of-the-art multiview and EC methods. We also showcase our method performs well with partial multiview data. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
35. Deep Decision Tree Transfer Boosting.
- Author
-
Jiang, Shuhui, Mao, Haiyi, Ding, Zhengming, and Fu, Yun
- Subjects
DECISION trees ,DATA distribution ,LABELS ,TASK analysis - Abstract
Instance transfer approaches consider source and target data together during the training process, and borrow examples from the source domain to augment the training data, when there is limited or no label in the target domain. Among them, boosting-based transfer learning methods (e.g., TrAdaBoost) are most widely used. When dealing with more complex data, we may consider the more complex hypotheses (e.g., a decision tree with deeper layers). However, with the fixed and high complexity of the hypotheses, TrAdaBoost and its variants may face the overfitting problems. Even worse, in the transfer learning scenario, a decision tree with deep layers may overfit different distribution data in the source domain. In this paper, we propose a new instance transfer learning method, i.e., Deep Decision Tree Transfer Boosting (DTrBoost), whose weights are learned and assigned to base learners by minimizing the data-dependent learning bounds across both source and target domains in terms of the Rademacher complexities. This guarantees that we can learn decision trees with deep layers without overfitting. The theorem proof and experimental results indicate the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
36. Discerning Feature Supported Encoder for Image Representation.
- Author
-
Wang, Shuyang, Ding, Zhengming, and Fu, Yun
- Subjects
- *
IMAGE representation , *IMAGE compression , *FEATURE selection , *ARCHITECTURE , *FEATURE extraction , *IMAGE reconstruction - Abstract
Inspired by the recent successes of deep architecture, the auto-encoder and its variants have been intensively explored on image clustering and classification tasks by learning effective feature representations. Conventional auto-encoder attempts to uncover the data’s intrinsic structure, by constraining the output to be as much identical to the input as possible, which denotes that the hidden representation could faithfully reconstruct the input data. One issue that arises, however, is that such representations might not be optimized for specific tasks, e.g., image classification and clustering, since it compresses not only the discriminative information but also a lot of redundant or even noise within data. In other words, not all hidden units would benefit the specific tasks, while partial units are mainly used to represent the task-irrelevant patterns. In this paper, a general framework named discerning feature supported encoder (DFSE) is proposed, which integrates the auto-encoder and feature selection together into a unified model. Specifically, the feature selection is adapted to learned hidden-layer features to capture the task-relevant ones from the task-irrelevant ones. Meanwhile, the selected hidden units could in turn encode more discriminability only on the selected task-relevant units. To this end, our proposed algorithm can generate more effective image representation by distinguishing the task-relevant features from the task-irrelevant ones. Two scenarios of the experiments on image classification and clustering are conducted to evaluate our algorithm. The experiments on several benchmarks demonstrate that our method can achieve better performance over the state-of-the-art approaches in two scenarios. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
37. Dual Low-Rank Decompositions for Robust Cross-View Learning.
- Author
-
Ding, Zhengming and Fu, Yun
- Subjects
- *
HUMAN facial recognition software , *MANIFOLDS (Mathematics) , *GRAPHIC methods , *DECOMPOSITION method , *LOW-rank matrices - Abstract
Cross-view data are very popular contemporarily, as different viewpoints or sensors attempt to richly represent data in various views. However, the cross-view data from different views present a significant divergence, that is, cross-view data from the same category have a lower similarity than those in different categories but within the same view. Considering that each cross-view sample is drawn from two intertwined manifold structures, i.e., class manifold and view manifold, in this paper, we propose a robust cross-view learning framework to seek a robust view-invariant low-dimensional space. Specifically, we develop a dual low-rank decomposition technique to unweave those intertwined manifold structures from one another in the learned space. Moreover, we design two discriminative graphs to constrain the dual low-rank decompositions by fully exploring the prior knowledge. Thus, our proposed algorithm is able to capture more within-class knowledge and mitigate the view divergence to obtain a more effective view-invariant feature extractor. Furthermore, our proposed method is very flexible in addressing such a challenging cross-view learning scenario that we only obtain the view information of the training data while with the view information of the evaluation data unknown. Experiments on face and object benchmarks demonstrate the effective performance of our designed model over the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
38. Semi-supervised Deep Domain Adaptation via Coupled Neural Networks.
- Author
-
Ding, Zhengming, Nasrabadi, Nasser M., and Fu, Yun
- Subjects
- *
NEURAL circuitry , *COMPUTER architecture , *FEATURE extraction , *MACHINE learning , *TASK analysis - Abstract
Domain adaptation is a promising technique when addressing limited or no labeled target data by borrowing well-labeled knowledge from the auxiliary source data. Recently, researchers have exploited multi-layer structures for discriminative feature learning to reduce the domain discrepancy. However, there are limited research efforts on simultaneously building a deep structure and a discriminative classifier over both labeled source and unlabeled target. In this paper, we propose a semi-supervised deep domain adaptation framework, in which the multi-layer feature extractor and a multi-class classifier are jointly learned to benefit from each other. Specifically, we develop a novel semi-supervised class-wise adaptation manner to fight off the conditional distribution mismatch between two domains by assigning a probabilistic label to each target sample, i.e., multiple class labels with different probabilities. Furthermore, a multi-class classifier is simultaneously trained on labeled source and unlabeled target samples in a semi-supervised fashion. In this way, the deep structure can formally alleviate the domain divergence and enhance the feature transferability. Experimental evaluations on several standard cross-domain benchmarks verify the superiority of our proposed approach. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
39. Domain Invariant and Class Discriminative Feature Learning for Visual Domain Adaptation.
- Author
-
Li, Shuang, Song, Shiji, Huang, Gao, Ding, Zhengming, and Wu, Cheng
- Subjects
INVARIANTS (Mathematics) ,LEARNING ,MATHEMATICAL symmetry ,DATA analysis ,ARTIFICIAL neural networks - Abstract
Domain adaptation manages to build an effective target classifier or regression model for unlabeled target data by utilizing the well-labeled source data but lying different distributions. Intuitively, to address domain shift problem, it is crucial to learn domain invariant features across domains, and most existing approaches have concentrated on it. However, they often do not directly constrain the learned features to be class discriminative for both source and target data, which is of vital importance for the final classification. Therefore, in this paper, we put forward a novel feature learning method for domain adaptation to construct both domain invariant and class discriminative representations, referred to as DICD. Specifically, DICD is to learn a latent feature space with important data properties preserved, which reduces the domain difference by jointly matching the marginal and class-conditional distributions of both domains, and simultaneously maximizes the inter-class dispersion and minimizes the intra-class scatter as much as possible. Experiments in this paper have demonstrated that the class discriminative properties will dramatically alleviate the cross-domain distribution inconsistency, which further boosts the classification performance. Moreover, we show that exploring both domain invariance and class discriminativeness of the learned representations can be integrated into one optimization framework, and the optimal solution can be derived effectively by solving a generalized eigen-decomposition problem. Comprehensive experiments on several visual cross-domain classification tasks verify that DICD can outperform the competitors significantly. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
40. Ensemble Subspace Segmentation Under Blockwise Constraints.
- Author
-
Zhao, Handong, Ding, Zhengming, and Fu, Yun
- Subjects
- *
COMPUTER algorithms , *IMAGE processing , *GRAPH theory , *GRAPHIC methods , *ARTIFICIAL intelligence - Abstract
The graph-based subspace segmentation technique has garnered a lot of attention in the visual data representation problem. In general, data (e.g., tracks of moving objects) are drawn from multiple linear subspaces. Thus, how to build a block-diagonal affinity matrix is the critical problem. In this paper, we propose a novel graph-based method, Ensemble Subspace Segmentation under Blockwise constraints (ESSB), which unifies least squares regression and a locality preserving graph regularizer into an ensemble learning framework. Specifically, compact encoding using least squares regression coefficients helps achieve a block-diagonal representation matrix among all samples. Meanwhile, the locality preserving regularizer tends to capture the intrinsic local structure, which further enhances the block-diagonal property. Both the blockwise efforts, i.e., least squares regression and the sparse regularizer, work jointly and are formulated in the ensemble learning framework, making ESSB more robust and efficient, especially when handling high-dimensional data. Finally, an efficient optimization solution based on inexact augmented Lagrange multiplier is derived with theoretical time complexity analysis. To demonstrate the effectiveness of the proposed method, we consider three different applications: face clustering, object clustering, and motion segmentation. Extensive results of both accuracy and normalized mutual information on four benchmarks, i.e., YaleB, ORL, COIL and Hopkins155, are reported. Also, the evaluations of computational cost are provided, based on which the superiority of our proposed method in both accuracy and efficiency is demonstrated compared with 12 baseline algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
41. Toward Resolution-Invariant Person Reidentification via Projective Dictionary Learning.
- Author
-
Li, Kai, Ding, Zhengming, Li, Sheng, and Fu, Yun
- Subjects
- *
IDENTIFICATION , *LEARNING strategies , *LEARNING - Abstract
Person reidentification (ReID) has recently been widely investigated for its vital role in surveillance and forensics applications. This paper addresses the low-resolution (LR) person ReID problem, which is of great practical meaning because pedestrians are often captured in LRs by surveillance cameras. Existing methods cope with this problem via some complicated and time-consuming strategies, making them less favorable, in practice, and meanwhile, their performances are far from satisfactory. Instead, we solve this problem by developing a discriminative semicoupled projective dictionary learning (DSPDL) model, which adopts the efficient projective dictionary learning strategy, and jointly learns a pair of dictionaries and a mapping function to model the correspondence of the cross-view data. A parameterless cross-view graph regularizer incorporating both positive and negative pair information is designed to enhance the discriminability of the dictionaries. Another weakness of existing approaches to this problem is that they are only applicable for the scenario where the cross-camera image sets have a globally uniform resolution gap. This fact undermines their practicality because the resolution gaps between cross-camera images often vary person by person in practice. To overcome this hurdle, we extend the proposed DSPDL model to the variational resolution gap scenario, basically by learning multiple pairs of dictionaries and multiple mapping functions. A novel technique is proposed to rerank and fuse the results obtained from all dictionary pairs. Experiments on five public data sets show the proposed method achieves superior performances to the state-of-the-art ones. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
42. Deep Transfer Low-Rank Coding for Cross-Domain Learning.
- Author
-
Ding, Zhengming and Fu, Yun
- Subjects
- *
BINOCULAR vision , *VISUAL accommodation , *KNOWLEDGE transfer , *MACHINE learning , *DEEP learning , *CONVOLUTIONAL neural networks , *MULTILAYER perceptrons - Abstract
Transfer learning has attracted great attention to facilitate the sparsely labeled or unlabeled target learning by leveraging previously well-established source domain through knowledge transfer. Recent activities on transfer learning attempt to build deep architectures to better fight off cross-domain divergences by extracting more effective features. However, its generalizability would decrease greatly due to the domain mismatch enlarges, particularly at the top layers. In this paper, we develop a novel deep transfer low-rank coding based on deep convolutional neural networks, where we investigate multilayer low-rank coding at the top task-specific layers. Specifically, multilayer common dictionaries shared across two domains are obtained to bridge the domain gap such that more enriched domain-invariant knowledge can be captured through a layerwise fashion. With rank minimization on the new codings, our model manages to preserve the global structures across source and target, and thus, similar samples of two domains tend to gather together for effective knowledge transfer. Furthermore, domain/classwise adaption terms are integrated to guide the effective coding optimization in a semisupervised manner, so the marginal and conditional disparities of two domains will be alleviated. Experimental results on three visual domain adaptation benchmarks verify the effectiveness of our proposed approach on boosting the recognition performance for the target domain, by comparing it with other state-of-the-art deep transfer learning. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
43. Task-driven deep transfer learning for image classification.
- Author
-
Ding, Zhengming, Nasrabadi, Nasser M, and Fu, Yun
- Published
- 2016
- Full Text
- View/download PDF
44. Transfer learning for image classification with incomplete multiple sources.
- Author
-
Ding, Zhengming, Shao, Ming, and Fu, Yun
- Published
- 2016
- Full Text
- View/download PDF
45. RGB-D Action Recognition.
- Author
-
Jia, Chengcheng, Kong, Yu, Ding, Zhengming, and Fu, Yun
- Published
- 2016
- Full Text
- View/download PDF
46. Deep Robust Encoder Through Locality Preserving Low-Rank Dictionary.
- Author
-
Ding, Zhengming, Shao, Ming, and Fu, Yun
- Published
- 2016
- Full Text
- View/download PDF
47. Robust Multiview Data Analysis Through Collective Low-Rank Subspace.
- Author
-
Ding, Zhengming and Fu, Yun
- Subjects
- *
DATA analysis , *LOW-rank matrices , *NEAR infrared radiation , *SUBSPACES (Mathematics) , *ROBUST statistics - Abstract
Multiview data are of great abundance in real-world applications, since various viewpoints and multiple sensors desire to represent the data in a better way. Conventional multiview learning methods aimed to learn multiple view-specific transformations meanwhile assumed the view knowledge of training, and test data were available in advance. However, they would fail when we do not have any prior knowledge for the probe data’s view information, since the correct view-specific projections cannot be utilized to extract effective feature representations. In this paper, we develop a collective low-rank subspace (CLRS) algorithm to deal with this problem in multiview data analysis. CLRS attempts to reduce the semantic gap across multiple views through seeking a view-free low-rank projection shared by multiple view-specific transformations. Moreover, we exploit low-rank reconstruction to build a bridge between the view-specific features and those view-free ones transformed with the CLRS. Furthermore, a supervised cross-view regularizer is developed to couple the within-class data across different views to make the learned collective subspace more discriminative. Our CLRS makes our algorithm more flexible when addressing the challenging issue without any prior knowledge of the probe data’s view information. To that end, two different settings of experiments on several multiview benchmarks are designed to evaluate the proposed approach. Experimental results have verified the effective performance of our proposed method by comparing with the state-of-the-art algorithms. [ABSTRACT FROM PUBLISHER]
- Published
- 2018
- Full Text
- View/download PDF
48. Incomplete Multisource Transfer Learning.
- Author
-
Ding, Zhengming, Shao, Ming, and Fu, Yun
- Subjects
- *
LEARNING , *DATA analysis - Abstract
Transfer learning is generally exploited to adapt well-established source knowledge for learning tasks in weakly labeled or unlabeled target domain. Nowadays, it is common to see multiple sources available for knowledge transfer, each of which, however, may not include complete classes information of the target domain. Naively merging multiple sources together would lead to inferior results due to the large divergence among multiple sources. In this paper, we attempt to utilize incomplete multiple sources for effective knowledge transfer to facilitate the learning task in target domain. To this end, we propose an incomplete multisource transfer learning through two directional knowledge transfer, i.e., cross-domain transfer from each source to target, and cross-source transfer. In particular, in cross-domain direction, we deploy latent low-rank transfer learning guided by iterative structure learning to transfer knowledge from each single source to target domain. This practice reinforces to compensate for any missing data in each source by the complete target data. While in cross-source direction, unsupervised manifold regularizer and effective multisource alignment are explored to jointly compensate for missing data from one portion of source to another. In this way, both marginal and conditional distribution discrepancy in two directions would be mitigated. Experimental results on standard cross-domain benchmarks and synthetic data sets demonstrate the effectiveness of our proposed model in knowledge transfer from incomplete multiple sources. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
49. Sparse low-rank fusion based deep features for missing modality face recognition.
- Author
-
Shao, Ming, Ding, Zhengming, and Fu, Yun
- Published
- 2015
- Full Text
- View/download PDF
50. Discriminative low-rank metric learning for face recognition.
- Author
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Ding, Zhengming, Suh, Sungjoo, Han, Jae-Joon, Choi, Changkyu, and Fu, Yun
- Published
- 2015
- Full Text
- View/download PDF
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