bethgelab / AnalysisBySynthesis
Adversarially Robust Neural Network on MNIST.
☆64Updated 2 years ago
Related projects ⓘ
Alternatives and complementary repositories for AnalysisBySynthesis
- Code for "Robustness May Be at Odds with Accuracy"☆93Updated last year
- Pytorch Adversarial Attack Framework☆78Updated 5 years ago
- Semisupervised learning for adversarial robustness https://arxiv.org/pdf/1905.13736.pdf☆137Updated 4 years ago
- Provable Robustness of ReLU networks via Maximization of Linear Regions [AISTATS 2019]☆31Updated 4 years ago
- Code for the paper "Understanding Generalization through Visualizations"☆60Updated 3 years ago
- Understanding and Improving Fast Adversarial Training [NeurIPS 2020]☆95Updated 3 years ago
- ☆87Updated 4 months ago
- Pytorch - Adversarial Training☆26Updated 6 years ago
- Visualization of Adversarial Examples☆31Updated 6 years ago
- Official TensorFlow Implementation of Adversarial Training for Free! which trains robust models at no extra cost compared to natural trai…☆173Updated 6 months ago
- Investigating the robustness of state-of-the-art CNN architectures to simple spatial transformations.☆49Updated 5 years ago
- Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks, in ICCV 2019☆59Updated 5 years ago
- Max Mahalanobis Training (ICML 2018 + ICLR 2020)☆89Updated 3 years ago
- Datasets for the paper "Adversarial Examples are not Bugs, They Are Features"☆184Updated 4 years ago
- Code for the unrestricted adversarial examples paper (NeurIPS 2018)☆63Updated 5 years ago
- Source code for the paper "Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness"☆26Updated 4 years ago
- Code for FAB-attack☆32Updated 4 years ago
- Code for "Learning Perceptually-Aligned Representations via Adversarial Robustness"☆159Updated 4 years ago
- ☆154Updated 3 years ago
- Code for the paper "Adversarial Training and Robustness for Multiple Perturbations", NeurIPS 2019☆46Updated last year
- A rich-documented PyTorch implementation of Carlini-Wagner's L2 attack.☆59Updated 6 years ago
- ☆31Updated 4 years ago
- RayS: A Ray Searching Method for Hard-label Adversarial Attack (KDD2020)☆56Updated 4 years ago
- Further improve robustness of mixup-trained models in inference (ICLR 2020)☆60Updated 4 years ago
- Code for ICLR2020 "Improving Adversarial Robustness Requires Revisiting Misclassified Examples"☆144Updated 4 years ago
- Implemented CURE algorithm from robustness via curvature regularization and vice versa☆29Updated last year
- CLEVER (Cross-Lipschitz Extreme Value for nEtwork Robustness) is a robustness metric for deep neural networks☆61Updated 3 years ago
- Official implementation for paper: A New Defense Against Adversarial Images: Turning a Weakness into a Strength☆37Updated 4 years ago
- Code for paper "Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality".☆122Updated 4 years ago
- Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)☆99Updated 2 years ago