Alternatives and similar repositories for pretraining-distribution-shift-robustness:

Users that are interested in pretraining-distribution-shift-robustness 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 last year
• thestephencasper / latent_adversarial_training
  ☆19Updated 5 months ago
• MadryLab / rethinking-backdoor-attacks
  ☆16Updated last year
• MadryLab / failure-directions
  Distilling Model Failures as Directions in Latent Space
  ☆46Updated last year
• MadryLab / journey-TRAK
  Code for the paper "The Journey, Not the Destination: How Data Guides Diffusion Models"
  ☆19Updated last year
• MadryLab / dataset-interfaces
  Dataset Interfaces: Diagnosing Model Failures Using Controllable Counterfactual Generation
  ☆44Updated last year
• MadryLab / BREEDS-Benchmarks
  ☆55Updated 4 years ago
• mueller-mp / SAM-ON
  ☆34Updated 11 months ago
• ethz-spylab / realistic-adv-examples
  Code for the paper "Evading Black-box Classifiers Without Breaking Eggs" [SaTML 2024]
  ☆19Updated 9 months ago
• vasusingla / simple-data-attribution
  A simple and efficient baseline for data attribution
  ☆11Updated last year
• MadryLab / datamodels
  ☆28Updated last year
• harshays / inputgradients
  Do input gradients highlight discriminative features? [NeurIPS 2021] (https://arxiv.org/abs/2102.12781)
  ☆13Updated 2 years ago
• JonasGeiping / dataaugs
  ☆17Updated 2 years ago
• pdejorge / N-FGSM
  Official repo for the paper "Make Some Noise: Reliable and Efficient Single-Step Adversarial Training" (https://arxiv.org/abs/2202.01181)
  ☆25Updated 2 years ago
• Bai-YT / AdaptiveSmoothing
  Implementation of the paper "Improving the Accuracy-Robustness Trade-off of Classifiers via Adaptive Smoothing".
  ☆11Updated 11 months ago
• hamidkazemi22 / CLIPInversion
  What do we learn from inverting CLIP models?
  ☆48Updated 10 months ago
• MadryLab / modeldiff
  ModelDiff: A Framework for Comparing Learning Algorithms
  ☆54Updated last year
• r-three / mats
  ☆27Updated 6 months ago
• facebookresearch / BalancingGroups
  Simple data balancing baselines for worst-group-accuracy benchmarks.
  ☆41Updated last year
• izmailovpavel / spurious_feature_learning
  ☆43Updated 2 years ago
• NYU-DICE-Lab / circumventing-concept-erasure
  ☆17Updated last year
• acmi-lab / RLSbench
  Code and results accompanying our paper titled RLSbench: Domain Adaptation under Relaxed Label Shift
  ☆34Updated last year
• tml-epfl / sam-low-rank-features
  Sharpness-Aware Minimization Leads to Low-Rank Features [NeurIPS 2023]
  ☆25Updated last year
• facebookresearch / ModelRatatouille
  Recycling diverse models
  ☆44Updated 2 years ago
• PolinaKirichenko / deep_feature_reweighting
  ☆107Updated last year
• singlasahil14 / salient_imagenet
  Code for the ICLR 2022 paper. Salient Imagenet: How to discover spurious features in deep learning?
  ☆36Updated 2 years ago
• OODRobustBench / OODRobustBench
  OODRobustBench: a Benchmark and Large-Scale Analysis of Adversarial Robustness under Distribution Shift. ICML 2024 and ICLRW-DMLR 2024
  ☆19Updated 5 months ago
• facebookresearch / Whac-A-Mole
  Code for the paper "A Whac-A-Mole Dilemma Shortcuts Come in Multiples Where Mitigating One Amplifies Others"
  ☆47Updated 6 months ago
• kaiwenzha / contrastive-poisoning
  [ICLR 2023, Spotlight] Indiscriminate Poisoning Attacks on Unsupervised Contrastive Learning
  ☆29Updated 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