Joint cross-domain classification and subspace learning for unsupervised adaptation

Domain adaptation aims at adapting the knowledge acquired on a source domain to a new different but related target domain. Several approaches have beenproposed for classification tasks in the unsupervised scenario, where no labeled target data are available. Most of the attention has been dedicated to searching a new domain-invariant representation, leaving the definition of the prediction function to a second stage. Here we propose to learn both jointly. Specifically we learn the source subspace that best matches the target subspace while at the same time minimizing a regularized misclassification loss. We provide an alternating optimization technique based on stochastic sub-gradient descent to solve the learning problem and we demonstrate its performance on several domain adaptation tasks.Comment: Paper is under consideration at Pattern Recognition Letter

Adversarial Training Based Multi-Source Unsupervised Domain Adaptation for Sentiment Analysis

Multi-source unsupervised domain adaptation (MS-UDA) for sentiment analysis (SA) aims to leverage useful information in multiple source domains to help do SA in an unlabeled target domain that has no supervised information. Existing algorithms of MS-UDA either only exploit the shared features, i.e., the domain-invariant information, or based on some weak assumption in NLP, e.g., smoothness assumption. To avoid these problems, we propose two transfer learning frameworks based on the multi-source domain adaptation methodology for SA by combining the source hypotheses to derive a good target hypothesis. The key feature of the first framework is a novel Weighting Scheme based Unsupervised Domain Adaptation framework (WS-UDA), which combine the source classifiers to acquire pseudo labels for target instances directly. While the second framework is a Two-Stage Training based Unsupervised Domain Adaptation framework (2ST-UDA), which further exploits these pseudo labels to train a target private extractor. Importantly, the weights assigned to each source classifier are based on the relations between target instances and source domains, which measured by a discriminator through the adversarial training. Furthermore, through the same discriminator, we also fulfill the separation of shared features and private features. Experimental results on two SA datasets demonstrate the promising performance of our frameworks, which outperforms unsupervised state-of-the-art competitors

Domain Consistency Regularization for Unsupervised Multi-source Domain Adaptive Classification

Deep learning-based multi-source unsupervised domain adaptation (MUDA) has been actively studied in recent years. Compared with single-source unsupervised domain adaptation (SUDA), domain shift in MUDA exists not only between the source and target domains but also among multiple source domains. Most existing MUDA algorithms focus on extracting domain-invariant representations among all domains whereas the task-specific decision boundaries among classes are largely neglected. In this paper, we propose an end-to-end trainable network that exploits domain Consistency Regularization for unsupervised Multi-source domain Adaptive classification (CRMA). CRMA aligns not only the distributions of each pair of source and target domains but also that of all domains. For each pair of source and target domains, we employ an intra-domain consistency to regularize a pair of domain-specific classifiers to achieve intra-domain alignment. In addition, we design an inter-domain consistency that targets joint inter-domain alignment among all domains. To address different similarities between multiple source domains and the target domain, we design an authorization strategy that assigns different authorities to domain-specific classifiers adaptively for optimal pseudo label prediction and self-training. Extensive experiments show that CRMA tackles unsupervised domain adaptation effectively under a multi-source setup and achieves superior adaptation consistently across multiple MUDA datasets

Revisiting the Domain Shift and Sample Uncertainty in Multi-source Active Domain Transfer

Active Domain Adaptation (ADA) aims to maximally boost model adaptation in a new target domain by actively selecting a limited number of target data to annotate.This setting neglects the more practical scenario where training data are collected from multiple sources. This motivates us to target a new and challenging setting of knowledge transfer that extends ADA from a single source domain to multiple source domains, termed Multi-source Active Domain Adaptation (MADA). Not surprisingly, we find that most traditional ADA methods cannot work directly in such a setting, mainly due to the excessive domain gap introduced by all the source domains and thus their uncertainty-aware sample selection can easily become miscalibrated under the multi-domain shifts. Considering this, we propose a Dynamic integrated uncertainty valuation framework(Detective) that comprehensively consider the domain shift between multi-source domains and target domain to detect the informative target samples. Specifically, the leverages a dynamic Domain Adaptation(DA) model that learns how to adapt the model's parameters to fit the union of multi-source domains. This enables an approximate single-source domain modeling by the dynamic model. We then comprehensively measure both domain uncertainty and predictive uncertainty in the target domain to detect informative target samples using evidential deep learning, thereby mitigating uncertainty miscalibration. Furthermore, we introduce a contextual diversity-aware calculator to enhance the diversity of the selected samples. Experiments demonstrate that our solution outperforms existing methods by a considerable margin on three domain adaptation benchmarks.Comment: arXiv admin note: text overlap with arXiv:2302.13824 by other author