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Unsupervised domain adaptation (UDA) has recently become an appealing research topic in visual recognition, since it exploits all accessible well-labeled source data to train a model with high generalization on target domain without any annotations. However, due to the significant domain discrepancy, the bottleneck for UDA is to learn effective domain-invariant feature representations. To fight off such an obstacle, we propose a novel cross-domain learning framework named Maximum Structural Generation Discrepancy (MSGD) to accurately estimate and mitigate domain shift via introducing an intermediate domain. First, the cross-domain topological structure is explored to propagate target samples to generate a novel intermediate domain paired with the specific source instances. The intermediate domain plays as the bridge to gradually reduce distribution divergence across source and target domains. Concretely, the similar category semantic across source and intermediate features tends to naturally conduct the class-level alignment on eliminating their domain shift. In terms of no target annotation, the domain-level alignment manner is suitable to narrow down the distance between intermediate and target domains. Moreover, to produce high-quality generative instances, we develop the class-driven collaborative translation (CDCT) module to generate class-consistent cross-domain samples in each mini-batch with the assistance of pseudo-labels. Extensive experimental analyses on five domain adaptation benchmarks demonstrate the effectiveness of our MSGD on solving UDA problem.

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.

Unsupervised domain adaptation without accessing expensive annotation processes of target data has achieved remarkable successes in semantic segmentation. However, most existing state-of-the-art methods cannot explore whether semantic representations across domains are transferable or not, which may result in the negative transfer brought by irrelevant knowledge. To tackle this challenge, in this paper, we develop a novel Knowledge Aggregation-induced Transferability Perception (KATP) for unsupervised domain adaptation, which is a pioneering attempt to distinguish transferable or untransferable knowledge across domains. Specifically, the KATP module is designed to quantify which semantic knowledge across domains is transferable, by incorporating transferability information propagation from global category-wise prototypes. Based on KATP, we design a novel KATP Adaptation Network (KATPAN) to determine where and how to transfer. The KATPAN contains a transferable appearance translation module T_A() and a transferable representation augmentation module T_R(), where both modules construct a virtuous circle of performance promotion. T_A() develops a transferability-aware information bottleneck to highlight where to adapt transferable visual characterizations and modality information; T_R() explores how to augment transferable representations while abandoning untransferable information, and promotes the translation performance of T_A() in return. Experiments on several representative datasets and a medical dataset support the state-of-the-art performance of our model.

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.

Deep domain adaptation methods have achieved appealing performance by learning transferable representations from a well-labeled source domain to a different but related unlabeled target domain. Most existing works assume source and target data share the identical label space, which is often difficult to be satisfied in many real-world applications. With the emergence of big data, there is a more practical scenario called partial domain adaptation, where we are always accessible to a more large-scale source domain while working on a relative small-scale target domain. In this case, the conventional domain adaptation assumption should be relaxed, and the target label space tends to be a subset of the source label space. Intuitively, reinforcing the positive effects of the most relevant source subclasses and reducing the negative impacts of irrelevant source subclasses are of vital importance to address partial domain adaptation challenge. This paper proposes an efficiently-implemented Deep Residual Correction Network (DRCN) by plugging one residual block into the source network along with the task-specific feature layer, which effectively enhances the adaptation from source to target and explicitly weakens the influence from the irrelevant source classes. Specifically, the plugged residual block, which consists of several fully-connected layers, could deepen basic network and boost its feature representation capability correspondingly. Moreover, we design a weighted class-wise domain alignment loss to couple two domains by matching the feature distributions of shared classes between source and target. Comprehensive experiments on partial, traditional and fine-grained cross-domain visual recognition demonstrate that DRCN is superior to the competitive deep domain adaptation approaches.

Spectral clustering has become one of the most effective clustering algorithms. We in this work explore the problem of spectral clustering in a lifelong learning framework termed as Generalized Lifelong Spectral Clustering (GL 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 task by What and How to lifelong learn from past tasks. For what to lifelong learn, our GL 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 a new clustering task arrives, the intuition here for how to lifelong learn is that GL 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. To the end, empirical comparisons on several benchmark datasets show the effectiveness of our GL 2 SC, in comparison with several state-of-the-art spectral clustering models.

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.

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.

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.