Code for "Learning Perceptually-Aligned Representations via Adversarial Robustness"
☆164Mar 19, 2020Updated 6 years ago
Alternatives and similar repositories for robust_representations
Users that are interested in robust_representations are comparing it to the libraries listed below
Sorting:
- Notebooks for reproducing the paper "Computer Vision with a Single (Robust) Classifier"☆129Oct 24, 2019Updated 6 years ago
- A library for experimenting with, training and evaluating neural networks, with a focus on adversarial robustness.☆944Jan 11, 2024Updated 2 years ago
- Code for "Robustness May Be at Odds with Accuracy"☆91Mar 24, 2023Updated 2 years ago
- Datasets for the paper "Adversarial Examples are not Bugs, They Are Features"☆187Sep 17, 2020Updated 5 years ago
- TRADES (TRadeoff-inspired Adversarial DEfense via Surrogate-loss minimization)☆554Mar 30, 2023Updated 2 years ago
- ImageNet classifier with state-of-the-art adversarial robustness☆684Dec 31, 2019Updated 6 years ago
- A challenge to explore adversarial robustness of neural networks on CIFAR10.☆507Aug 30, 2021Updated 4 years ago
- Project page for our paper: Interpreting Adversarially Trained Convolutional Neural Networks☆66Aug 8, 2019Updated 6 years ago
- CVPR'19 experiments with (on-manifold) adversarial examples.☆43Feb 27, 2020Updated 6 years ago
- Code for ICLR2020 "Improving Adversarial Robustness Requires Revisiting Misclassified Examples"☆153Oct 15, 2020Updated 5 years ago
- Code for the paper "Adversarial Training and Robustness for Multiple Perturbations", NeurIPS 2019☆47Dec 8, 2022Updated 3 years ago
- ☆11Sep 20, 2019Updated 6 years ago
- A Toolbox for Adversarial Robustness Research☆1,365Sep 14, 2023Updated 2 years ago
- Code for our nips19 paper: You Only Propagate Once: Accelerating Adversarial Training Via Maximal Principle☆181Jul 25, 2024Updated last year
- Provable adversarial robustness at ImageNet scale☆407May 20, 2019Updated 6 years ago
- ☆162Feb 26, 2021Updated 5 years ago
- Understanding and Improving Fast Adversarial Training [NeurIPS 2020]☆96Sep 23, 2021Updated 4 years ago
- Related papers for robust machine learning☆566May 25, 2023Updated 2 years ago
- Code for our NeurIPS 2019 *spotlight* "Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers"☆228Nov 9, 2019Updated 6 years ago
- Source code for the paper "Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness"☆25Feb 12, 2020Updated 6 years ago
- Code for "Testing Robustness Against Unforeseen Adversaries"☆80Jul 25, 2024Updated last year
- Logit Pairing Methods Can Fool Gradient-Based Attacks [NeurIPS 2018 Workshop on Security in Machine Learning]☆19Dec 2, 2018Updated 7 years ago
- Empirical tricks for training robust models (ICLR 2021)☆258May 25, 2023Updated 2 years ago
- A method for training neural networks that are provably robust to adversarial attacks.