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Source free Single and Multi target Unsupervised Domain Adaptation
ReID SFDA - Dual-Region Augmentation (Foreground Noise Background Shuffle) for robust person re-identification (ReID) and source-free domain adaptation (SFDA).
python == 3.6.8 - pytorch ==1.1.0 - torchvision == 0.3.0 - numpy, scipy, sklearn, PIL, argparse, tqdm
Unsupervised Multi-source Domain Adaptation Without Access to Source Data (CVPR '21 Oral)
Shuffle PatchMix (SPM) for Source-Free Domain Adaptation (ICIP 2025); patch-shuffle augmentation + confidence-margin pseudo-labels. New SOTA on PACS (+7.3%), strong results on DomainNet-126 and VisDA-C.
The main features of prasannapulakurthi/spm are: Multi-Target Adaptation, Source Free Domain Adaptation.
Projects with overlapping indexed features include: vcl-iisc/conmix — Source free Single and Multi target Unsupervised Domain Adaptation. prasannapulakurthi/foreground-background-augmentation — ReID + SFDA - Dual-Region Augmentation (Foreground Noise + Background Shuffle) for robust person re-identification… davidpengucf/sfdahpe — python == 3.6.8 - pytorch ==1.1.0 - torchvision == 0.3.0 - numpy, scipy, sklearn, PIL, argparse, tqdm. haifengxia/sfda. jxhuang0508/hcl — <Model Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Data> in NIPS 2021. driptarc/decision — Unsupervised Multi-source Domain Adaptation Without Access to Source Data (CVPR '21 Oral).