MadryLab / robust_representationsLinks
Code for "Learning Perceptually-Aligned Representations via Adversarial Robustness"
☆163Updated 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:
- Datasets for the paper "Adversarial Examples are not Bugs, They Are Features"☆187Updated 5 years ago
- Notebooks for reproducing the paper "Computer Vision with a Single (Robust) Classifier"☆129Updated 6 years ago
- Code for "Robustness May Be at Odds with Accuracy"☆91Updated 2 years ago
- ☆88Updated last year
- Code for our NeurIPS 2019 *spotlight* "Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers"☆228Updated 6 years ago
- Project page for our paper: Interpreting Adversarially Trained Convolutional Neural Networks☆66Updated 6 years ago
- Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks, in ICCV 2019☆58Updated 6 years ago
- Code for our nips19 paper: You Only Propagate Once: Accelerating Adversarial Training Via Maximal Principle☆178Updated last year
- ☆21Updated last year
- A Closer Look at Accuracy vs. Robustness☆88Updated 4 years ago
- Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)☆100Updated 3 years ago
- Official TensorFlow Implementation of Adversarial Training for Free! which trains robust models at no extra cost compared to natural trai…☆177Updated last year
- ☆31Updated 5 years ago
- Code for the CVPR 2019 article "Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses"☆137Updated 5 years ago
- [ICML 2019] ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation☆54Updated last month
- Spatially Transformed Adversarial Examples with TensorFlow☆75Updated 7 years ago
- Code for the paper "Understanding Generalization through Visualizations"☆64Updated 4 years ago
- Semisupervised learning for adversarial robustness https://arxiv.org/pdf/1905.13736.pdf☆141Updated 5 years ago
- A pytorch implementation of our jacobian regularizer to encourage learning representations more robust to input perturbations.☆129Updated 2 years ago
- Code for "Testing Robustness Against Unforeseen Adversaries"☆80Updated last year
- Codebase for "Exploring the Landscape of Spatial Robustness" (ICML'19, https://arxiv.org/abs/1712.02779).☆26Updated 6 years ago
- ☆142Updated 5 years ago
- Provably defending pretrained classifiers including the Azure, Google, AWS, and Clarifai APIs☆99Updated 4 years ago
- Max Mahalanobis Training (ICML 2018 + ICLR 2020)☆90Updated 4 years ago
- Pytorch Adversarial Attack Framework☆78Updated 6 years ago
- Investigating the robustness of state-of-the-art CNN architectures to simple spatial transformations.☆49Updated 6 years ago
- Code for the paper "Adversarial Training and Robustness for Multiple Perturbations", NeurIPS 2019☆47Updated 2 years ago
- CVPR'19 experiments with (on-manifold) adversarial examples.☆45Updated 5 years ago
- A look at some simple autoencoders for the Cifar10 dataset, including a denoising autoencoder. Python code included.☆64Updated 7 years ago
- A PyTorch baseline attack example for the NIPS 2017 adversarial competition☆86Updated 8 years ago