Alternatives and similar repositories for ProjNorm:

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

• deeplearning-wisc / Spurious_OOD
  ☆22Updated 2 years ago
• yaodongyu / BiasVariance-AdversarialTraining
  ☆8Updated 4 years ago
• tml-epfl / sharpness-vs-generalization
  A modern look at the relationship between sharpness and generalization [ICML 2023]
  ☆43Updated 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
• deeplearning-wisc / gradnorm_ood
  On the Importance of Gradients for Detecting Distributional Shifts in the Wild
  ☆55Updated 2 years ago
• saurabhgarg1996 / ATC_code
  Code and results accompanying our paper titled Leveraging Unlabeled Data to Predict Out-of-Distribution Performance at ICLR 2022
  ☆11Updated 2 years ago
• huaxiuyao / LISA
  LISA for ICML 2022
  ☆47Updated last year
• ryoungj / optdom
  [ICLR'22] Self-supervised learning optimally robust representations for domain shift.
  ☆23Updated 3 years ago
• garyzhao / ME-ADA
  "Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness" (NeurIPS 2020).
  ☆50Updated 4 years ago
• uiuctml / ISR
  Invariant-feature Subspace Recovery (ISR)
  ☆23Updated 2 years ago
• ynysjtu / ood_bench
  ☆48Updated 2 years ago
• mpagli / Agree-to-Disagree
  Repo for the paper: "Agree to Disagree: Diversity through Disagreement for Better Transferability"
  ☆35Updated 2 years ago
• TjuJianyu / RFC
  ☆12Updated last year
• thudzj / ScalableBDL
  Code for "BayesAdapter: Being Bayesian, Inexpensively and Robustly, via Bayeisan Fine-tuning"
  ☆31Updated 8 months ago
• tml-epfl / adv-training-corruptions
  On the effectiveness of adversarial training against common corruptions [UAI 2022]
  ☆30Updated 2 years ago
• izmailovpavel / spurious_feature_learning
  ☆44Updated 2 years ago
• mueller-mp / SAM-ON
  ☆34Updated last year
• JonasGeiping / fullbatchtraining
  Training vision models with full-batch gradient descent and regularization
  ☆37Updated 2 years ago
• JonasGeiping / dataaugs
  ☆17Updated 2 years ago
• PeymanMorteza / GEM
  ☆19Updated 3 years ago
• LijieFan / AdvCL
  [NeurIPS 2021] “When does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning?”
  ☆48Updated 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
• MadryLab / BREEDS-Benchmarks
  ☆54Updated 4 years ago
• luoyan407 / congruency
  [TPAMI 2019] The implementation for "Direction Concentration Learning: Enhancing Congruency in Machine Learning"
  ☆23Updated 5 years ago
• nblt / RWP
  ☆11Updated 2 years ago
• davidstutz / confidence-calibrated-adversarial-training
  Implementation of Confidence-Calibrated Adversarial Training (CCAT).
  ☆45Updated 4 years ago
• jkatzsam / woods_ood
  ☆16Updated 2 years ago
• yangarbiter / rare-spurious-correlation
  Understanding Rare Spurious Correlations in Neural Network
  ☆12Updated 2 years ago
• harshays / inputgradients
  Do input gradients highlight discriminative features? [NeurIPS 2021] (https://arxiv.org/abs/2102.12781)
  ☆13Updated 2 years ago
• salesforce / ensemble-of-averages
  ☆29Updated last year