# Improving Unsupervised Domain Adaptation with Variational Information Bottleneck

arXiv: https://arxiv.org/abs/1911.09310 · HTML: https://mlsanity.com/paper/1911.09310/

## Key facts

- Cited by 5 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 21 Nov 2019.

## Built on by (most cited first, 5 of 5)

- [Information-theoretic regularization for Multi-source Domain Adaptation](https://mlsanity.com/paper/2104.01568/) (2021)
- [Cross Domain Robot Imitation with Invariant Representation](https://mlsanity.com/paper/2109.05940/) (2021)
- [SIMSHIFT: A Benchmark for Adapting Neural Surrogates to Distribution Shifts](https://mlsanity.com/paper/2506.12007/) (2025)
- [IT-RUDA: Information Theory Assisted Robust Unsupervised Domain Adaptation](https://mlsanity.com/paper/2210.12947/) (2022)
- [Domain Generalization without Excess Empirical Risk](https://mlsanity.com/paper/2308.15856/) (2023)

## Abstract (arXiv)

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.