  Limited transferability hinders the performance of deep learning models when
applied to new application scenarios. Recently, unsupervised domain adaptation
(UDA) has achieved significant progress in addressing this issue via learning
domain-invariant features. However, large domain shifts and the sample scarcity
in the target domain make existing UDA methods achieve suboptimal performance.
To alleviate these issues, we propose a plug-and-play Diffusion-based Target
Sampler (DTS) to generate high fidelity and diversity pseudo target samples. By
introducing class-conditional information, the labels of the generated target
samples can be controlled. The generated samples can well simulate the data
distribution of the target domain and help existing UDA methods transfer from
the source domain to the target domain more easily, thus improving the transfer
performance. Extensive experiments on various benchmarks demonstrate that the
performance of existing UDA methods can be greatly improved through the
proposed DTS method.
