Alternatives and similar repositories for rare-spurious-correlation:

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

• MadryLab / DebuggableDeepNetworks
  ☆38Updated 3 years ago
• tml-epfl / sharpness-vs-generalization
  A modern look at the relationship between sharpness and generalization [ICML 2023]
  ☆43Updated last year
• harshays / inputgradients
  Do input gradients highlight discriminative features? [NeurIPS 2021] (https://arxiv.org/abs/2102.12781)
  ☆13Updated 2 years ago
• MadryLab / failure-directions
  Distilling Model Failures as Directions in Latent Space
  ☆46Updated 2 years ago
• ecreager / eiil
  Code for Environment Inference for Invariant Learning (ICML 2021 Paper)
  ☆50Updated 3 years ago
• JonasGeiping / dataaugs
  ☆17Updated 2 years ago
• zleizzo / datadeletion
  ☆14Updated 5 years ago
• uiuctml / fair-classification
  Post-processing for fair classification
  ☆13Updated 2 months ago
• izmailovpavel / spurious_feature_learning
  ☆44Updated 2 years ago
• facebookresearch / BalancingGroups
  Simple data balancing baselines for worst-group-accuracy benchmarks.
  ☆42Updated 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"
  ☆18Updated 2 years ago
• tml-epfl / understanding-sam
  Towards Understanding Sharpness-Aware Minimization [ICML 2022]
  ☆35Updated 2 years ago
• tml-epfl / adv-training-corruptions
  On the effectiveness of adversarial training against common corruptions [UAI 2022]
  ☆30Updated 2 years ago
• anniesch / jtt
  Code for "Just Train Twice: Improving Group Robustness without Training Group Information"
  ☆70Updated 10 months ago
• MadryLab / pretraining-distribution-shift-robustness
  ☆14Updated last year
• MadryLab / journey-TRAK
  Code for the paper "The Journey, Not the Destination: How Data Guides Diffusion Models"
  ☆22Updated last year
• huaxiuyao / LISA
  LISA for ICML 2022
  ☆47Updated last year
• gortizji / linearized-networks
  Source code of "What can linearized neural networks actually say about generalization?
  ☆20Updated 3 years ago
• yjhuangcd / local-lipschitz
  Official implementation for Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds (NeurIPS, 2021).
  ☆23Updated 2 years ago
• ryoungj / optdom
  [ICLR'22] Self-supervised learning optimally robust representations for domain shift.
  ☆23Updated 3 years ago
• mueller-mp / SAM-ON
  ☆34Updated last year
• TjuJianyu / RFC
  ☆12Updated last year
• ahujak / IB-IRM
  ☆30Updated 3 years ago
• ethz-spylab / realistic-adv-examples
  Code for the paper "Evading Black-box Classifiers Without Breaking Eggs" [SaTML 2024]
  ☆20Updated 11 months ago
• ElisaNguyen / bayesian-tda
  Code for NeurIPS'23 paper "A Bayesian Approach To Analysing Training Data Attribution In Deep Learning"
  ☆15Updated last year
• liuchen11 / AdversaryLossLandscape
  On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them [NeurIPS 2020]
  ☆35Updated 3 years ago
• harshays / simplicitybiaspitfalls
  The Pitfalls of Simplicity Bias in Neural Networks [NeurIPS 2020] (http://arxiv.org/abs/2006.07710v2)
  ☆39Updated last year
• deeplearning-wisc / Spurious_OOD
  ☆22Updated 2 years ago
• MadryLab / BREEDS-Benchmarks
  ☆54Updated 4 years ago
• xu-ji / information-bottleneck
  Deep Learning & Information Bottleneck
  ☆58Updated last year