Improving Unsupervised Domain Adaptation with Variational Information Bottleneck
This work addresses domain adaptation for machine learning models when target labels are unavailable, representing an incremental improvement over existing methods.
The paper tackled the problem of insufficient feature matching in unsupervised domain adaptation by proposing a variational bottleneck method that enforces the feature extractor to focus on task-relevant information, resulting in significant performance improvements over state-of-the-art methods across three benchmark datasets.
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.