Alternatives and similar repositories for BAR:

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

• uvavision / Balanced-Datasets-Are-Not-Enough
  [ICCV 2019] Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations
  ☆31Updated 3 years ago
• princetonvisualai / DomainBiasMitigation
  ☆73Updated 4 years ago
• alinlab / LfF
  Learning from Failure: Training Debiased Classifier from Biased Classifier (NeurIPS 2020)
  ☆90Updated 4 years ago
• PolinaKirichenko / deep_feature_reweighting
  ☆107Updated last year
• zhihengli-UR / DebiAN
  Official code of "Discover and Mitigate Unknown Biases with Debiasing Alternate Networks" (ECCV 2022)
  ☆23Updated 2 years ago
• facebookresearch / Whac-A-Mole
  Code for the paper "A Whac-A-Mole Dilemma Shortcuts Come in Multiples Where Mitigating One Amplifies Others"
  ☆47Updated 7 months ago
• deeplearning-wisc / vit-spurious-robustness
  ☆24Updated 2 years ago
• MadryLab / backgrounds_challenge
  ☆140Updated 4 years ago
• singlasahil14 / salient_imagenet
  Code for the ICLR 2022 paper. Salient Imagenet: How to discover spurious features in deep learning?
  ☆38Updated 2 years ago
• ryoungj / optdom
  [ICLR'22] Self-supervised learning optimally robust representations for domain shift.
  ☆23Updated 3 years ago
• erobic / bias-mitigators
  Demonstrates failures of bias mitigation methods under varying types/levels of biases (WACV 2021)
  ☆25Updated 10 months ago
• EIDOSLAB / entangling-disentangling-bias
  EnD: Entangling and Disentangling deep representations for bias correction | CVPR21 https://doi.org/10.1109/CVPR46437.2021.01330
  ☆16Updated 3 years ago
• Weixin-Liang / MetaShift
  MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts and Training Conflicts (ICLR 2022)
  ☆109Updated 2 years ago
• deeplearning-wisc / Spurious_OOD
  ☆21Updated 2 years ago
• deeplearning-wisc / gradnorm_ood
  On the Importance of Gradients for Detecting Distributional Shifts in the Wild
  ☆55Updated 2 years ago
• MadryLab / BREEDS-Benchmarks
  ☆54Updated 4 years ago
• VITA-Group / SDCLR
  [ICML 2021] “ Self-Damaging Contrastive Learning”, Ziyu Jiang, Tianlong Chen, Bobak Mortazavi, Zhangyang Wang
  ☆63Updated 3 years ago
• princetonvisualai / HIVE
  HIVE: Evaluating the Human Interpretability of Visual Explanations (ECCV 2022)
  ☆19Updated 2 years ago
• anniesch / jtt
  Code for "Just Train Twice: Improving Group Robustness without Training Group Information"
  ☆70Updated 9 months ago
• YugeTen / fish
  Official implementation of paper Gradient Matching for Domain Generalization
  ☆118Updated 3 years ago
• yoonholee / DivDis
  ☆39Updated 2 years ago
• izmailovpavel / spurious_feature_learning
  ☆44Updated 2 years ago
• aliasgharkhani / Masktune
  ☆25Updated 2 years ago
• feidfoe / learning-not-to-learn
  [CVPR2019]Learning Not to Learn : An adversarial method to train deep neural networks with biased data
  ☆111Updated 4 years ago
• songweige / Contrastive-Learning-with-Non-Semantic-Negatives
  Robust Contrastive Learning Using Negative Samples with Diminished Semantics (NeurIPS 2021)
  ☆38Updated 3 years ago
• zhihengli-UR / discover_unknown_biases
  Official code of "Discover the Unknown Biased Attribute of an Image Classifier" (ICCV 2021)
  ☆19Updated 3 years ago
• deeplearning-wisc / large_scale_ood
  MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space
  ☆90Updated 3 years ago
• chingyaoc / fair-mixup
  ICLR 2021, Fair Mixup: Fairness via Interpolation
  ☆55Updated 3 years ago
• garyzhao / ME-ADA
  "Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness" (NeurIPS 2020).
  ☆50Updated 4 years ago
• harshays / simplicitybiaspitfalls
  The Pitfalls of Simplicity Bias in Neural Networks [NeurIPS 2020] (http://arxiv.org/abs/2006.07710v2)
  ☆39Updated last year