title: Shuffle PatchMix Augmentation with Confidence-Margin Weighted
    Pseudo-Labels for Enhanced Source-Free Domain Adaptation
summary: 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
paper_authors._id: 683e626d1417d1073375ed9d
paper_authors.name: Prasanna Reddy Pulakurthi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-03T08:46:21.114Z
paper_authors._id: 68090a58090c3a12a2806615
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/ap4nNCSMXic4-wC09SkpO.png
paper_authors.fullname: PRASANNA REDDY PULAKURTHI
paper_authors.type: user
paper_authors.user: prasannareddyp
paper_authors._id: 683e626d1417d1073375ed9e
paper_authors.name: Majid Rabbani
paper_authors._id: 683e626d1417d1073375ed9f
paper_authors.name: Jamison Heard
paper_authors._id: 683e626d1417d1073375eda0
paper_authors.name: Sohail Dianat
paper_authors._id: 683e626d1417d1073375eda1
paper_authors.name: Celso M. de Melo
paper_authors._id: 683e626d1417d1073375eda2
paper_authors.name: Raghuveer Rao
paper_title: Shuffle PatchMix Augmentation with Confidence-Margin Weighted
    Pseudo-Labels for Enhanced Source-Free Domain Adaptation
paper_summary: 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
paper_ai_summary: 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.
paper_ai_keywords: Source-Free Domain Adaptation Shuffle PatchMix reweighting strategy pseudo-labels PACS VisDA-C DomainNet-126