Shuffle PatchMix Augmentation with Confidence-Margin Weighted
  Pseudo-Labels for Enhanced Source-Free Domain Adaptation
Authors: name: Prasanna Reddy Pulakurthi, name: Majid Rabbani, name: Jamison Heard, name: Sohail Dianat, name: Celso M. de Melo, name: Raghuveer Rao

This work investigates Source-Free Domain Adaptation (SFDA), where a model
adapts to a target domain without access to source data. A new augmentation
technique, Shuffle PatchMix (SPM), and a novel reweighting strategy are
introduced to enhance performance. SPM shuffles and blends image patches to
generate diverse and challenging augmentations, while the reweighting strategy
prioritizes reliable pseudo-labels to mitigate label noise. These techniques
are particularly effective on smaller datasets like PACS, where overfitting and
pseudo-label noise pose greater risks. State-of-the-art results are achieved on
three major benchmarks: PACS, VisDA-C, and DomainNet-126. Notably, on PACS,
improvements of 7.3% (79.4% to 86.7%) and 7.2% are observed in single-target
and multi-target settings, respectively, while gains of 2.8% and 0.7% are
attained on DomainNet-126 and VisDA-C. This combination of advanced
augmentation and robust pseudo-label reweighting establishes a new benchmark
for SFDA. The code is available at: https://github.com/PrasannaPulakurthi/SPM