Alternatives and similar repositories for SDAT

Users that are interested in SDAT are comparing it to the libraries listed below

• NaJaeMin92 / FixBi
  Official code for the CVPR 2021 paper "FixBi: Bridging Domain Spaces for Unsupervised Domain Adaptation"
  ☆66Updated 3 years ago
• Liuhong99 / CST
  Code release for "Cycle Self-Training for Domain Adaptation" (NeurIPS 2021)
  ☆53Updated 3 years ago
• DianCh / AdaContrast
  Official repository of CVPR '22 paper "Contrastive Test-Time Adaptation"
  ☆102Updated 3 years ago
• ChrisAllenMing / LtC-MSDA
  Implementation of Learning to Combine: Knowledge Aggregation for Multi-Source Domain Adaptation (ECCV 2020).
  ☆73Updated 3 years ago
• tim-learn / SHOT-plus
  code for our TPAMI 2021 paper "Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer"
  ☆121Updated 2 years ago
• microsoft / UDA
  Unsupervised Domain Adaptation for Computer Vision Tasks
  ☆108Updated 3 years ago
• ZyeZhang / DaC
  Code for Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning (NeurIPS 2022).
  ☆42Updated 2 years ago
• Albert0147 / NRC_SFDA
  Code for our NeurIPS 2021 paper 'Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation'
  ☆77Updated 2 years ago
• tim-learn / DINE
  code for our CVPR 2022 paper "DINE: Domain Adaptation from Single and Multiple Black-box Predictors"
  ☆81Updated last year
• youngryan1993 / SFDA-SourceFreeDA
  Published in IEEE Transactions on Artificial Intelligence
  ☆56Updated 3 years ago
• Albert0147 / G-SFDA
  code for our ICCV 2021 paper 'Generalized Source-free Domain Adaptation'
  ☆107Updated 3 years ago
• tim-learn / BA3US
  code for our ECCV 2020 paper "A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation"
  ☆40Updated 2 years ago
• acmi-lab / Open-Set-Label-Shift
  Code and results accompanying our paper titled Domain Adaptation under Open Set Label Shift
  ☆31Updated 2 years ago
• lileicv / PDEN
  ☆37Updated 4 years ago
• YyzHarry / multi-domain-imbalance
  [ECCV 2022] Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization, and Beyond
  ☆137Updated 2 years ago
• BIT-DA / EADA
  [AAAI 2022] Official Implementation of Active Learning for Domain Adaptation: An Energy-based Approach https://arxiv.org/abs/2112.01406
  ☆88Updated last year
• ygjwd12345 / TransDA
  Official pytorch implement for “Transformer-Based Source-Free Domain Adaptation”
  ☆68Updated 2 years ago
• Zhongying-Deng / DAC-Net
  Pytorch implementation of DAC-Net ("Zhongying Deng, Kaiyang Zhou, Yongxin Yang, Tao Xiang. Domain Attention Consistency for Multi-Source …
  ☆24Updated 3 years ago
• korawat-tanwisuth / Proto_DA
  ☆47Updated 3 years ago
• Solacex / Domain-Consensus-Clustering
  [CVPR2021] Domain Consensus Clustering for Universal Domain Adaptation
  ☆110Updated 3 years ago
• sshan-zhao / DG_via_ER
  Domain Generalization via Entropy Regularization, NeurIPS'20
  ☆66Updated 4 years ago
• LeeHyuck / ABC
  pytorch implementation of ABC : Auxiliary Balanced Classifier for Class-imbalanced Semi-supervised Learning
  ☆38Updated 8 months ago
• VisionLearningGroup / CDS
  CDS: Cross-Domain Self-supervised Pre-training
  ☆27Updated 3 years ago
• Learning-group123 / CAiDA
  Code for CAiDA
  ☆24Updated 3 years ago
• LeoXinhaoLee / AMS
  Code released for our ECCV 2022 paper "Interpretable Open-Set Domain Adaptation via Angular Margin Separation".
  ☆20Updated 2 years ago
• VisionLearningGroup / Benchmark_Domain_Transfer
  ☆18Updated 3 years ago
• virajprabhu / CLUE
  PyTorch code for Active Domain Adaptation via Clustering Uncertainty-weighted Embeddings (ICCV 2021)
  ☆44Updated last year
• KaiyangZhou / ssdg-benchmark
  Benchmarks for semi-supervised domain generalization.
  ☆69Updated 2 years ago
• zhengzangw / PCS-FUDA
  Official implementation of PCS in essay "Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptation"
  ☆106Updated 4 years ago
• frank-xwang / debiased-pseudo-labeling
  [CVPR 2022] Pytorch implementation for “Debiased Learning from Naturally Imbalanced Pseudo-Labels”
  ☆99Updated 2 years ago