Alternatives and similar repositories for rare-spurious-correlation

Users that are interested in rare-spurious-correlation are comparing it to the libraries listed below

• tml-epfl / sharpness-vs-generalization
  A modern look at the relationship between sharpness and generalization [ICML 2023]
  ☆43Updated 2 years ago
• anniesch / jtt
  Code for "Just Train Twice: Improving Group Robustness without Training Group Information"
  ☆72Updated last year
• zleizzo / datadeletion
  ☆14Updated 5 years ago
• mueller-mp / SAM-ON
  ☆34Updated last year
• tml-epfl / adv-training-corruptions
  On the effectiveness of adversarial training against common corruptions [UAI 2022]
  ☆30Updated 3 years ago
• ecreager / eiil
  Code for Environment Inference for Invariant Learning (ICML 2021 Paper)
  ☆50Updated 4 years ago
• deeplearning-wisc / Spurious_OOD
  ☆23Updated 3 years ago
• harshays / inputgradients
  Do input gradients highlight discriminative features? [NeurIPS 2021] (https://arxiv.org/abs/2102.12781)
  ☆13Updated 2 years ago
• izmailovpavel / spurious_feature_learning
  ☆46Updated 2 years ago
• modestyachts / cifar-10.2
  Host CIFAR-10.2 Data Set
  ☆13Updated 3 years ago
• ethz-spylab / realistic-adv-examples
  Code for the paper "Evading Black-box Classifiers Without Breaking Eggs" [SaTML 2024]
  ☆21Updated last year
• MadryLab / BREEDS-Benchmarks
  ☆55Updated 5 years ago
• JonasGeiping / dataaugs
  ☆18Updated 2 years ago
• tml-epfl / understanding-sam
  Towards Understanding Sharpness-Aware Minimization [ICML 2022]
  ☆35Updated 3 years ago
• harshays / simplicitybiaspitfalls
  The Pitfalls of Simplicity Bias in Neural Networks [NeurIPS 2020] (http://arxiv.org/abs/2006.07710v2)
  ☆41Updated last year
• fra31 / robust-finetuning
  Code relative to "Adversarial robustness against multiple and single $l_p$-threat models via quick fine-tuning of robust classifiers"
  ☆19Updated 2 years ago
• tding1 / Neural-Collapse
  [NeurIPS 2021] A Geometric Analysis of Neural Collapse with Unconstrained Features
  ☆58Updated 3 years ago
• PolinaKirichenko / deep_feature_reweighting
  ☆108Updated last year
• liuchen11 / AdversaryLossLandscape
  On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them [NeurIPS 2020]
  ☆36Updated 4 years ago
• ryoungj / optdom
  [ICLR'22] Self-supervised learning optimally robust representations for domain shift.
  ☆24Updated 3 years ago
• uiuctml / fair-classification
  Post-processing for fair classification
  ☆16Updated 2 months ago
• BorealisAI / mma_training
  Code for the paper "MMA Training: Direct Input Space Margin Maximization through Adversarial Training"
  ☆34Updated 5 years ago
• huaxiuyao / LISA
  LISA for ICML 2022
  ☆51Updated 2 years ago
• MadryLab / rethinking-backdoor-attacks
  ☆16Updated 2 years ago
• alexrame / diwa
  DiWA: Diverse Weight Averaging for Out-of-Distribution Generalization
  ☆31Updated 2 years ago
• singlasahil14 / salient_imagenet
  Code for the ICLR 2022 paper. Salient Imagenet: How to discover spurious features in deep learning?
  ☆40Updated 3 years ago
• davidstutz / confidence-calibrated-adversarial-training
  Implementation of Confidence-Calibrated Adversarial Training (CCAT).
  ☆45Updated 5 years ago
• inspire-group / proxy-distributions
  [ICLR 2022 official code] Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?
  ☆29Updated 3 years ago
• MadryLab / DebuggableDeepNetworks
  ☆38Updated 4 years ago
• MadryLab / failure-directions
  Distilling Model Failures as Directions in Latent Space
  ☆47Updated 2 years ago