Alternatives and similar repositories for Cost-Sensitive-Robustness

Users that are interested in Cost-Sensitive-Robustness are comparing it to the libraries listed below

• dtak / adversarial-robustness-public
  Code for AAAI 2018 accepted paper: "Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing the…
  ☆55Updated 3 years ago
• xuanqing94 / BayesianDefense
  Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network
  ☆62Updated 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 7 years ago
• yk / icml19_public
  ☆22Updated 5 years ago
• davidstutz / disentangling-robustness-generalization
  CVPR'19 experiments with (on-manifold) adversarial examples.
  ☆45Updated 5 years ago
• deepgenerativeclassifier / DeepBayes
  Code release for the ICML 2019 paper "Are generative classifiers more robust to adversarial attacks?"
  ☆23Updated 6 years ago
• hendrycks / pre-training
  Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)
  ☆100Updated 3 years ago
• chenhongge / RobustTrees
  [ICML 2019, 20 min long talk] Robust Decision Trees Against Adversarial Examples
  ☆68Updated 5 months 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 5 years ago
• cetmann / robustness-interpretability
  Code for the Paper 'On the Connection Between Adversarial Robustness and Saliency Map Interpretability' by C. Etmann, S. Lunz, P. Maass, …
  ☆16Updated 6 years ago
• max-andr / cross-lipschitz
  Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation [NeurIPS 2017]
  ☆18Updated 7 years ago
• enyac-group / Renofeation
  ☆19Updated 4 years ago
• yangarbiter / robust-local-lipschitz
  A Closer Look at Accuracy vs. Robustness
  ☆88Updated 4 years ago
• locuslab / perturbation_learning
  Learning perturbation sets for robust machine learning
  ☆65Updated 4 years ago
• ftramer / MultiRobustness
  Code for the paper "Adversarial Training and Robustness for Multiple Perturbations", NeurIPS 2019
  ☆47Updated 3 years ago
• alvinchangw / IGAM_CVPR2020
  Implementation for What it Thinks is Important is Important: Robustness Transfers through Input Gradients (CVPR 2020 Oral)
  ☆16Updated 2 years ago
• hendrycks / fooling
  Code for the Adversarial Image Detectors and a Saliency Map
  ☆12Updated 8 years ago
• labsix / adversarial-logit-pairing-analysis
  Analysis of Adversarial Logit Pairing
  ☆60Updated 7 years ago
• ZhaoyangLyu / POPQORN
  An Algorithm to Quantify Robustness of Recurrent Neural Networks
  ☆49Updated 5 years ago
• BorealisAI / mma_training
  Code for the paper "MMA Training: Direct Input Space Margin Maximization through Adversarial Training"
  ☆34Updated 5 years ago
• max-andr / provably-robust-boosting
  Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks [NeurIPS 2019]
  ☆50Updated 5 years ago
• max-andr / provable-robustness-max-linear-regions
  Provable Robustness of ReLU networks via Maximization of Linear Regions [AISTATS 2019]
  ☆31Updated 5 years ago
• tginart / deletion-efficient-kmeans
  Research prototype of deletion efficient k-means algorithms
  ☆24Updated 5 years ago
• P2333 / Max-Mahalanobis-Training
  Max Mahalanobis Training (ICML 2018 + ICLR 2020)
  ☆90Updated 4 years ago
• M4xim4l / InNOutRobustness
  Adversarial Robustness on In- and Out-Distribution Improves Explainability
  ☆12Updated 3 years ago
• P2333 / Reverse-Cross-Entropy
  Reverse Cross Entropy for Adversarial Detection (NeurIPS 2018)
  ☆47Updated 4 years ago
• tcwangshiqi-columbia / Interval-Attack
  Interval attacks (adversarial ML)
  ☆21Updated 6 years ago
• bhanML / SIGUA
  ICML'20: SIGUA: Forgetting May Make Learning with Noisy Labels More Robust
  ☆17Updated 5 years ago
• UMBCvision / fooling_network_interpretation
  Official PyTorch implementation for our ICCV 2019 paper - Fooling Network Interpretation in Image Classification
  ☆24Updated 6 years ago
• facebookresearch / fisher_information_loss
  This code reproduces the results of the paper, "Measuring Data Leakage in Machine-Learning Models with Fisher Information"
  ☆50Updated 4 years ago