  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.
