MadryLab / robust_representationsLinks
Code for "Learning Perceptually-Aligned Representations via Adversarial Robustness"
☆160Updated 5 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"☆128Updated 5 years ago
- Code for "Robustness May Be at Odds with Accuracy"☆91Updated 2 years ago
- Datasets for the paper "Adversarial Examples are not Bugs, They Are Features"☆187Updated 4 years ago
- Project page for our paper: Interpreting Adversarially Trained Convolutional Neural Networks☆66Updated 5 years ago
- Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks, in ICCV 2019☆58Updated 5 years ago
- Pytorch Adversarial Attack Framework☆78Updated 6 years ago
- Code for the CVPR 2019 article "Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses"☆135Updated 4 years ago
- Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)☆100Updated 3 years ago
- Investigating the robustness of state-of-the-art CNN architectures to simple spatial transformations.☆49Updated 5 years ago
- ☆87Updated 10 months ago
- Code for the unrestricted adversarial examples paper (NeurIPS 2018)☆64Updated 5 years ago
- Understanding and Improving Fast Adversarial Training [NeurIPS 2020]☆95Updated 3 years ago
- Code for our nips19 paper: You Only Propagate Once: Accelerating Adversarial Training Via Maximal Principle☆175Updated 10 months ago
- Data, code & materials from the paper "Generalisation in humans and deep neural networks" (NeurIPS 2018)☆96Updated last year
- Adversarially Robust Neural Network on MNIST.☆64Updated 3 years ago
- Semisupervised learning for adversarial robustness https://arxiv.org/pdf/1905.13736.pdf☆142Updated 5 years ago
- Official TensorFlow Implementation of Adversarial Training for Free! which trains robust models at no extra cost compared to natural trai…☆174Updated last year
- Max Mahalanobis Training (ICML 2018 + ICLR 2020)☆90Updated 4 years ago
- Smooth Adversarial Training☆67Updated 4 years ago
- Further improve robustness of mixup-trained models in inference (ICLR 2020)☆60Updated 4 years ago
- Code for "Testing Robustness Against Unforeseen Adversaries"