Improving Unsupervised Domain Adaptation with Variational Information Bottleneck

Abstract

Domain adaptation aims to leverage the supervision signal of source domain to obtain an accurate model for target domain, where the labels are not available. To leverage and adapt the label information from source domain, most existing methods employ a feature extracting function and match the marginal distributions of source and target domains in a shared feature space. In this paper, from the perspective of information theory, we show that representation matching is actually an insufficient constraint on the feature space for obtaining a model with good generalization performance in target domain. We then propose variational bottleneck domain adaptation (VBDA), a new domain adaptation method which improves feature transferability by explicitly enforcing the feature extractor to ignore the task-irrelevant factors and focus on the information that is essential to the task of interest for both source and target domains. Extensive experimental results demonstrate that VBDA significantly outperforms state-of-the-art methods across three domain adaptation benchmark datasets.

References in corpus (9)

• Auto-Encoding Variational Bayes
• The information bottleneck method
• Unsupervised Domain Adaptation with Residual Transfer Networks
• Asymmetric Tri-training for Unsupervised Domain Adaptation
• Adversarial Discriminative Domain Adaptation
• A DIRT-T Approach to Unsupervised Domain Adaptation
• Deep Variational Information Bottleneck
• On Learning Invariant Representation for Domain Adaptation
• Self-ensembling for visual domain adaptation

Cited by in corpus (1)

• Continuous Domain Adaptation with Variational Domain-Agnostic Feature Replay