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Domain Adaptation (DA) is a method for enhancing a model's performance on atarget domain with inadequate annotated data by applying the information themodel has acquired from a related source domain with sufficient labeled data.The escalating enforcement of data-privacy regulations like HIPAA, COPPA,FERPA, etc. have sparked a heightened interest in adapting models to noveldomains while circumventing the need for direct access to the source data, aproblem known as Source-Free Domain Adaptation (SFDA). In this paper, wepropose a novel framework for SFDA that generates source data using atext-to-image diffusion model trained on the target domain samples. Our methodstarts by training a text-to-image diffusion model on the labeled target domainsamples, which is then fine-tuned using the pre-trained source model togenerate samples close to the source data. Finally, we use Domain Adaptationtechniques to align the artificially generated source data with the targetdomain data, resulting in significant performance improvements of the model onthe target domain. Through extensive comparison against several baselines onthe standard Office-31, Office-Home, and VisDA benchmarks, we demonstrate theeffectiveness of our approach for the SFDA task.

Transcending Domains through Text-to-Image Diffusion: A Source-Free Approach to Domain Adaptation