Source-Free Domain Adaptation with Diffusion-Guided Source Data
  Generation
Shivang Chopra Suraj Kothawade Houda Aynaou Aman Chadha
This paper introduces a novel approach to leverage the generalizability
capability of Diffusion Models for Source-Free Domain Adaptation (DM-SFDA). Our
proposed DM-SFDA 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 apply established
unsupervised domain adaptation techniques to align the generated source images
with target domain data. We validate our approach through comprehensive
experiments across a range of datasets, including Office-31, Office-Home, and
VisDA. The results highlight significant improvements in SFDA performance,
showcasing the potential of diffusion models in generating contextually
relevant, domain-specific images.