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Domain adaptation techniques learn transferable knowledge from a source domain to a target domain and train models that generalize well in the target domain. Unfortunately, a majority of the existing techniques are only applicable to scenarios that the target-domain data in the task of interest is available for training, yet this is not often true in practice. In general, human beings are experts in generalization across domains. For example, a baby can easily identify the bear from a clipart image after learning this category of animal from the photo images. To reduce the gap between the generalization ability of human and that of machines, we propose a new solution to the challenging zero-shot domain adaptation (ZSDA) problem, where only a single source domain is available and the target domain for the task of interest is not accessible. Inspired by the observation that the knowledge about domain correlation can improve our generalization ability, we explore the correlation between source domain and target domain in an irrelevant knowledge task ([Formula: see text]-task), where dual-domain samples are available. We denote the task of interest as the question task ([Formula: see text]-task) and synthesize its non-accessible target-domain as such that these two tasks have the shared domain correlation. In order to realize our idea, we introduce a new network structure, i.e., conditional coupled generative adversarial networks (CoCoGAN), by extending the coupled generative adversarial networks (CoGAN) into a conditioning model. With a pair of coupling GANs, our CoCoGAN is able to capture the joint distribution of data samples across two domains and two tasks. For CoCoGAN training in a ZSDA task, we introduce three supervisory signals, i.e., semantic relationship consistency across domains, global representation alignment across tasks, and alignment consistency across domains. Experimental results demonstrate that our method can learn a suitable model for the non-accessible target domain and outperforms the existing state of the arts in both image classification and semantic segmentation.

Maximum likelihood classifier (MLC) and support vector machines (SVM) are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonparametric method in this context. Recently, it is found that SVM in some cases is equivalent to MLC in probabilistically modeling the learning process. In this paper, MLC and SVM are combined in learning and classification, which helps to yield probabilistic output for SVM and facilitate soft decision making. In total four groups of data are used for evaluations, covering sonar, vehicle, breast cancer, and DNA sequences. The data samples are characterized in terms of Gaussian/non-Gaussian distributed and balanced/unbalanced samples which are then further used for performance assessment in comparing the SVM and the combined SVM-MLC classifier. Interesting results are reported to indicate how the combined classifier may work under various conditions.

To improve the performance of ensemble techniques for temporal data clustering, we propose a novel bi-weighted ensemble in this paper to solve the initialization and automated model selection problems encountered by all HMM-based clustering techniques and their applications. Our proposed ensemble features in a bi-weighting scheme in the process of examining each partition and optimizing consensus function on these input partitions in accordance with their level of importance. Within our proposed scheme, the multiple partitions, generated by HMM-based K-models under different initializations, are optimally re-consolidated into a representation of bi-weighted hypergraph, and the final consensus partition is generated and optimized via the agglomerative clustering algorithm in association with a dendrogram-based similarity partitioning (DSPA). In comparison with the existing state of the arts, our proposed approach not only achieves the advantage that the number of clusters can be automatically determined, but also the superior clustering performances on a range of temporal datasets, including synthetic dataset, time series benchmark, and real-world motion trajectory datasets. [ABSTRACT FROM AUTHOR]

While existing logistic regression suffers from overfitting and often fails in considering structural information, we propose a novel matrix-based logistic regression to overcome the weakness. In the proposed method, 2D matrices are directly used to learn two groups of parameter vectors along each dimension without vectorization, which allows the proposed method to fully exploit the underlying structural information embedded inside the 2D matrices. Further, we add a joint ℓ2,1-norm on two parameter matrices, which are organized by aligning each group of parameter vectors in columns. This added co-regularization term has two roles--enhancing the effect of regularization and optimizing the rank during the learning process. With our proposed fast iterative solution, we carried out extensive experiments. The results show that in comparison to both the traditional tensor-based methods and the vector-based regression methods, our proposed solution achieves better performance for matrix data classifications. [ABSTRACT FROM AUTHOR]

Recent developments of virtual reality applications have accelerated the usage of cameras with wide-angle and telephoto/macro lens, which produce nonlinear radial lens distortions, such as barrel distortion and pincushion distortion. However, due to many reasons, the resolution of images with nonlinear lens distortions is often limited. In this paper, we address the image super-resolution (SR) for images with nonlinear lens distortions through the deep convolutional neutral network with residual learning, which can significantly improve the image quality before and after the camera calibration. The proposed deep learning network was trained using hundreds of simulated images and tested on real cameras with fisheye and macro lens. Experimental results show that the proposed image SR method outperforms state-of-the-art SR methods for various degrees of radial-based barrel and pincushion distortions. [ABSTRACT FROM AUTHOR]

Abstract: Recognition of handwritten Arabic cursive texts is a complex task due to the similarities between letters under different writing styles. In this paper, a word-based off-line recognition system is proposed, using Hidden Markov Models (HMMs). The method employed involves three stages, namely preprocessing, feature extraction and classification. First, words from input scripts are segmented and normalized. Then, a set of intensity features are extracted from each of the segmented words, which is based on a sliding window moving across each mirrored word image. Meanwhile, structure-like features are also extracted including number of subwords and diacritical marks. Finally, these features are applied in a combined scheme for classification. Intensity features are used to train a HMM classifier, whose results are re-ranked using structure-like features for improved recognition rate. In order to validate the proposed techniques, extensive experiments were carried out using the IFN/ENIT database which contains 32,492 handwritten Arabic words. The proposed algorithm yields superior results of improved accuracy in comparison with several typical methods. [Copyright &y& Elsevier]

Abstract: This paper presents a comparative study of two machine learning techniques for recognizing handwritten Arabic words, where hidden Markov models (HMMs) and dynamic Bayesian networks (DBNs) were evaluated. The work proposed is divided into three stages, namely preprocessing, feature extraction and classification. Preprocessing includes baseline estimation and normalization as well as segmentation. In the second stage, features are extracted from each of the normalized words, where a set of new features for handwritten Arabic words is proposed, based on a sliding window approach moving across the mirrored word image. The third stage is for classification and recognition, where machine learning is applied using HMMs and DBNs. In order to validate the techniques, extensive experiments were conducted using the IFN/ENIT database which contains 32,492 Arabic words. Experimental results and quantitative evaluations showed that HMM outperforms DBN in terms of higher recognition rate and lower complexity. [Copyright &y& Elsevier]