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.

本文提出了一种基于扩散的目标采样器（DTS），使用分类条件信息生成高保真度与多样性伪目标样本，用于改进现有无监督领域自适应方法的传输性能。大量实验表明，该方法可以显著提高现有的无监督领域自适应方法的性能。

基于扩散的目标采样器用于无监督领域自适应