Alternatives and similar repositories for disentangling-robustness-generalization

Users that are interested in disentangling-robustness-generalization are comparing it to the libraries listed below

• hendrycks / pre-training
  Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)
  ☆100Updated 3 years ago
• ftramer / MultiRobustness
  Code for the paper "Adversarial Training and Robustness for Multiple Perturbations", NeurIPS 2019
  ☆47Updated 2 years ago
• MadryLab / DebuggableDeepNetworks
  ☆38Updated 4 years ago
• BorealisAI / mma_training
  Code for the paper "MMA Training: Direct Input Space Margin Maximization through Adversarial Training"
  ☆34Updated 5 years ago
• MadryLab / spatial-pytorch
  Codebase for "Exploring the Landscape of Spatial Robustness" (ICML'19, https://arxiv.org/abs/1712.02779).
  ☆26Updated 5 years ago
• alvinchangw / JARN_ICLR2020
  Implementation for Jacobian Adversarially Regularized Networks for Robustness (ICLR 2020)
  ☆22Updated 5 years ago
• ShawnXYang / AT_HE
  ☆35Updated 4 years ago
• max-andr / provable-robustness-max-linear-regions
  Provable Robustness of ReLU networks via Maximization of Linear Regions [AISTATS 2019]
  ☆32Updated 4 years ago
• tml-epfl / adv-training-corruptions
  On the effectiveness of adversarial training against common corruptions [UAI 2022]
  ☆30Updated 3 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
• tml-epfl / understanding-fast-adv-training
  Understanding and Improving Fast Adversarial Training [NeurIPS 2020]
  ☆95Updated 3 years ago
• liuchen11 / AdversaryLossLandscape
  On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them [NeurIPS 2020]
  ☆36Updated 3 years ago
• deepgenerativeclassifier / DeepBayes
  Code release for the ICML 2019 paper "Are generative classifiers more robust to adversarial attacks?"
  ☆23Updated 6 years ago
• uds-lsv / evaluating-logit-pairing-methods
  Logit Pairing Methods Can Fool Gradient-Based Attacks [NeurIPS 2018 Workshop on Security in Machine Learning]
  ☆19Updated 6 years ago
• LTS4 / hold-me-tight
  Source code of "Hold me tight! Influence of discriminative features on deep network boundaries"
  ☆22Updated 3 years ago
• PKUAI26 / AT-CNN
  Project page for our paper: Interpreting Adversarially Trained Convolutional Neural Networks
  ☆66Updated 5 years ago
• yaodongyu / BiasVariance-AdversarialTraining
  ☆8Updated 4 years ago
• P2333 / Max-Mahalanobis-Training
  Max Mahalanobis Training (ICML 2018 + ICLR 2020)
  ☆90Updated 4 years ago
• torrvision / whoneedsadversaries
  Code implementing the experiments described in the NeurIPS 2018 paper "With Friends Like These, Who Needs Adversaries?".
  ☆13Updated 4 years ago
• locuslab / projected_sinkhorn
  ☆87Updated 11 months ago
• AlexMeinke / certified-certain-uncertainty
  A way to achieve uniform confidence far away from the training data.
  ☆38Updated 4 years ago
• dtak / adversarial-robustness-public
  Code for AAAI 2018 accepted paper: "Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing the…
  ☆55Updated 2 years ago
• locuslab / robust_union
  [ICML'20] Multi Steepest Descent (MSD) for robustness against the union of multiple perturbation models.
  ☆26Updated 11 months ago
• ftramer / Excessive-Invariance
  Source code for the paper "Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness"
  ☆25Updated 5 years ago
• yaircarmon / semisup-adv
  Semisupervised learning for adversarial robustness https://arxiv.org/pdf/1905.13736.pdf
  ☆142Updated 5 years ago
• MadryLab / BREEDS-Benchmarks
  ☆55Updated 4 years ago
• davidstutz / confidence-calibrated-adversarial-training
  Implementation of Confidence-Calibrated Adversarial Training (CCAT).
  ☆45Updated 4 years ago
• columbia / Metric_Learning_Adversarial_Robustness
  Code for NeurIPS 2019 Paper
  ☆47Updated 5 years ago
• yangarbiter / robust-local-lipschitz
  A Closer Look at Accuracy vs. Robustness
  ☆89Updated 4 years ago
• labsix / adversarial-logit-pairing-analysis
  Analysis of Adversarial Logit Pairing
  ☆60Updated 6 years ago