Alternatives and similar repositories for Breaking_Down_OOD_Detection

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

• j-cb / GOOD
  Provable Worst Case Guarantees for the Detection of Out-of-Distribution Data
  ☆13Updated 2 years ago
• schzhu / learning-adversarially-robust-representations
  Code for the paper: Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization (https://arxiv.org/abs/2…
  ☆23Updated 4 years ago
• tml-epfl / adv-training-corruptions
  On the effectiveness of adversarial training against common corruptions [UAI 2022]
  ☆30Updated 3 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
• 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
• cassidylaidlaw / perceptual-advex
  Code and data for the ICLR 2021 paper "Perceptual Adversarial Robustness: Defense Against Unseen Threat Models".
  ☆55Updated 3 years ago
• AlexMeinke / certified-certain-uncertainty
  A way to achieve uniform confidence far away from the training data.
  ☆38Updated 4 years ago
• deeplearning-wisc / Spurious_OOD
  ☆23Updated 3 years ago
• jh-jeong / smoothmix
  Code for the paper "SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Robustness" (NeurIPS 2021)
  ☆21Updated 2 years ago
• tml-epfl / sharpness-vs-generalization
  A modern look at the relationship between sharpness and generalization [ICML 2023]
  ☆43Updated last year
• locuslab / robust_union
  [ICML'20] Multi Steepest Descent (MSD) for robustness against the union of multiple perturbation models.
  ☆26Updated 11 months ago
• davidstutz / confidence-calibrated-adversarial-training
  Implementation of Confidence-Calibrated Adversarial Training (CCAT).
  ☆45Updated 4 years ago
• yangarbiter / rare-spurious-correlation
  Understanding Rare Spurious Correlations in Neural Network
  ☆12Updated 3 years ago
• yjhuangcd / local-lipschitz
  Official implementation for Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds (NeurIPS, 2021).
  ☆24Updated 2 years ago
• DequanWang / dent
  Fighting Gradients with Gradients: Dynamic Defenses against Adversarial Attacks
  ☆39Updated 4 years ago
• arpitbansal297 / Meta-Balance
  ☆24Updated 3 years ago
• ChaojianYu / Understanding-Robust-Overfitting
  Implementation for <Understanding Robust Overftting of Adversarial Training and Beyond> in ICML'22.
  ☆12Updated 2 years ago
• P2333 / SCORE
  A Self-Consistent Robust Error (ICML 2022)
  ☆68Updated 2 years ago
• thudzj / ScalableBDL
  Code for "BayesAdapter: Being Bayesian, Inexpensively and Robustly, via Bayeisan Fine-tuning"
  ☆31Updated 11 months ago
• jkatzsam / woods_ood
  ☆16Updated 3 years ago
• deepgenerativeclassifier / DeepBayes
  Code release for the ICML 2019 paper "Are generative classifiers more robust to adversarial attacks?"
  ☆23Updated 6 years ago
• liuchen11 / AdversaryLossLandscape
  On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them [NeurIPS 2020]
  ☆36Updated 3 years ago
• goldblum / AdversariallyRobustDistillation
  Pytorch implementation of Adversarially Robust Distillation (ARD)
  ☆59Updated 6 years ago
• MadryLab / DebuggableDeepNetworks
  ☆38Updated 4 years ago
• locuslab / perturbation_learning
  Learning perturbation sets for robust machine learning
  ☆65Updated 3 years ago
• MotasemAlfarra / Combating-Adversaries-with-Anti-Adversaries
  ☆10Updated 3 years ago
• dedeswim / vits-robustness-torch
  Code for the paper "A Light Recipe to Train Robust Vision Transformers" [SaTML 2023]
  ☆52Updated 2 years ago
• hammlab / LimitsOfUDA
  Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning. (Neurips 2021)
  ☆8Updated 3 years ago
• kaiwenzha / contrastive-poisoning
  [ICLR 2023, Spotlight] Indiscriminate Poisoning Attacks on Unsupervised Contrastive Learning
  ☆31Updated last year
• jfc43 / informative-outlier-mining
  We propose a theoretically motivated method, Adversarial Training with informative Outlier Mining (ATOM), which improves the robustness o…
  ☆57Updated 3 years ago