  Domain Adaptation (DA) is a method for enhancing a model's performance on a
target domain with inadequate annotated data by applying the information the
model 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 novel
domains while circumventing the need for direct access to the source data, a
problem known as Source-Free Domain Adaptation (SFDA). In this paper, we
propose a novel framework for SFDA that generates source data using a
text-to-image diffusion model trained on the target domain samples. Our method
starts by training a text-to-image diffusion model on the labeled target domain
samples, which is then fine-tuned using the pre-trained source model to
generate samples close to the source data. Finally, we use Domain Adaptation
techniques to align the artificially generated source data with the target
domain data, resulting in significant performance improvements of the model on
the target domain. Through extensive comparison against several baselines on
the standard Office-31, Office-Home, and VisDA benchmarks, we demonstrate the
effectiveness of our approach for the SFDA task.
