Unsupervised Domain Adaptation via Domain-Adaptive Diffusion

TL;DR

This paper introduces a novel domain-adaptive diffusion approach with mutual learning to improve unsupervised domain adaptation by gradually transforming data distributions and preserving semantics across large domain gaps.

Contribution

It proposes a new diffusion-based method with a mutual learning strategy to effectively handle large distribution discrepancies in UDA tasks.

Findings

Outperforms state-of-the-art methods on three UDA datasets.

Effectively decomposes large domain gaps into smaller, manageable transitions.

Enhances classification accuracy in target domains.

Abstract

Unsupervised Domain Adaptation (UDA) is quite challenging due to the large distribution discrepancy between the source domain and the target domain. Inspired by diffusion models which have strong capability to gradually convert data distributions across a large gap, we consider to explore the diffusion technique to handle the challenging UDA task. However, using diffusion models to convert data distribution across different domains is a non-trivial problem as the standard diffusion models generally perform conversion from the Gaussian distribution instead of from a specific domain distribution. Besides, during the conversion, the semantics of the source-domain data needs to be preserved for classification in the target domain. To tackle these problems, we propose a novel Domain-Adaptive Diffusion (DAD) module accompanied by a Mutual Learning Strategy (MLS), which can gradually convert data distribution from the source domain to the target domain while enabling the classification model to learn along the domain transition process. Consequently, our method successfully eases the challenge of UDA by decomposing the large domain gap into small ones and gradually enhancing the capacity of classification model to finally adapt to the target domain. Our method outperforms the current state-of-the-arts by a large margin on three widely used UDA datasets.