  This paper introduces a novel approach to leverage the generalizability of
Diffusion Models for Source-Free Domain Adaptation (DM-SFDA). Our proposed
DMSFDA method involves fine-tuning a pre-trained text-to-image diffusion model
to generate source domain images using features from the target images to guide
the diffusion process. Specifically, the pre-trained diffusion model is
fine-tuned to generate source samples that minimize entropy and maximize
confidence for the pre-trained source model. We then use a diffusion
model-based image mixup strategy to bridge the domain gap between the source
and target domains. We validate our approach through comprehensive experiments
across a range of datasets, including Office-31, Office-Home, and VisDA. The
results demonstrate significant improvements in SFDA performance, highlighting
the potential of diffusion models in generating contextually relevant,
domain-specific images.
