Shuffle PatchMix Augmentation with Confidence-Margin Weighted
  Pseudo-Labels for Enhanced Source-Free Domain Adaptation
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
683e626d1417d1073375ed9d
Prasanna Reddy Pulakurthi
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2025-06-03T08:46:21.114Z
68090a58090c3a12a2806615
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PRASANNA REDDY PULAKURTHI
user
prasannareddyp
683e626d1417d1073375ed9e
Majid Rabbani
683e626d1417d1073375ed9f
Jamison Heard
683e626d1417d1073375eda0
Sohail Dianat
683e626d1417d1073375eda1
Celso M. de Melo
683e626d1417d1073375eda2
Raghuveer Rao
Shuffle PatchMix Augmentation with Confidence-Margin Weighted
  Pseudo-Labels for Enhanced Source-Free Domain Adaptation
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
A new augmentation technique, Shuffle PatchMix, and a reweighting strategy improve performance in source-free domain adaptation, achieving state-of-the-art results on PACS, VisDA-C, and DomainNet-126 benchmarks.
Source-Free Domain Adaptation Shuffle PatchMix reweighting strategy pseudo-labels PACS VisDA-C DomainNet-126