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This repository provides the official implementation for "Source-free Domain Adaptation via Avatar Prototype Generation and Adaptation". (IJCAI2021)
The main features of scut-ailab/cpga are: Source Free Domain Adaptation.
Projects with overlapping indexed features include: davidpengucf/rain — python == 3.6.8 - pytorch ==1.1.0 - torchvision == 0.3.0 - numpy, scipy, sklearn, PIL, argparse, tqdm. davidpengucf/sfdahpe — python == 3.6.8 - pytorch ==1.1.0 - torchvision == 0.3.0 - numpy, scipy, sklearn, PIL, argparse, tqdm. driptarc/decision — Unsupervised Multi-source Domain Adaptation Without Access to Source Data (CVPR '21 Oral). haifengxia/sfda. jxhuang0508/hcl — <Model Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Data> in NIPS 2021. albert0147/g-sfda — code for our ICCV 2021 paper 'Generalized Source-free Domain Adaptation'.
python == 3.6.8 - pytorch ==1.1.0 - torchvision == 0.3.0 - numpy, scipy, sklearn, PIL, argparse, tqdm
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)
code for our ICCV 2021 paper 'Generalized Source-free Domain Adaptation'