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

• MadryLab / failure-directions
  Distilling Model Failures as Directions in Latent Space
  ☆47Updated 2 years ago
• MadryLab / datamodels-data
  Data for "Datamodels: Predicting Predictions with Training Data"
  ☆97Updated 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
• mueller-mp / SAM-ON
  ☆34Updated last year
• MadryLab / datamodels
  ☆29Updated 2 years ago
• yangarbiter / rare-spurious-correlation
  Understanding Rare Spurious Correlations in Neural Network
  ☆12Updated 3 years ago
• tml-epfl / sharpness-vs-generalization
  A modern look at the relationship between sharpness and generalization [ICML 2023]
  ☆43Updated last year
• MadryLab / modeldiff
  ModelDiff: A Framework for Comparing Learning Algorithms
  ☆59Updated last year
• ethz-spylab / realistic-adv-examples
  Code for the paper "Evading Black-box Classifiers Without Breaking Eggs" [SaTML 2024]
  ☆21Updated last year
• hamidkazemi22 / CLIPInversion
  What do we learn from inverting CLIP models?
  ☆55Updated last year
• thestephencasper / latent_adversarial_training
  ☆22Updated 11 months ago
• MadryLab / journey-TRAK
  Code for the paper "The Journey, Not the Destination: How Data Guides Diffusion Models"
  ☆24Updated last year
• PolinaKirichenko / deep_feature_reweighting
  ☆107Updated last year
• tml-epfl / sam-low-rank-features
  Sharpness-Aware Minimization Leads to Low-Rank Features [NeurIPS 2023]
  ☆28Updated last year
• MadryLab / BREEDS-Benchmarks
  ☆55Updated 4 years ago
• izmailovpavel / spurious_feature_learning
  ☆45Updated 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"
  ☆19Updated 2 years ago
• yolky / RFAD
  Code for the paper "Efficient Dataset Distillation using Random Feature Approximation"
  ☆37Updated 2 years ago
• alexrame / diwa
  DiWA: Diverse Weight Averaging for Out-of-Distribution Generalization
  ☆31Updated 2 years ago
• dedeswim / vits-robustness-torch
  Code for the paper "A Light Recipe to Train Robust Vision Transformers" [SaTML 2023]
  ☆52Updated 2 years ago
• uiuctml / fair-classification
  Post-processing for fair classification
  ☆15Updated 2 weeks ago
• aks2203 / easy-to-hard-data
  Pytorch Datasets for Easy-To-Hard
  ☆27Updated 6 months ago
• MadryLab / rethinking-backdoor-attacks
  ☆16Updated last year
• JonasGeiping / dataaugs
  ☆18Updated 2 years ago
• singlasahil14 / salient_imagenet
  Code for the ICLR 2022 paper. Salient Imagenet: How to discover spurious features in deep learning?
  ☆40Updated 2 years ago
• anniesch / jtt
  Code for "Just Train Twice: Improving Group Robustness without Training Group Information"
  ☆72Updated last year
• YanNeu / spurious_imagenet
  Spurious Features Everywhere - Large-Scale Detection of Harmful Spurious Features in ImageNet
  ☆32Updated last year
• LijieFan / AdvCL
  [NeurIPS 2021] “When does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning?”
  ☆48Updated 3 years ago
• modestyachts / cifar-10.2
  Host CIFAR-10.2 Data Set
  ☆13Updated 3 years ago