MadryLab / robustness_applicationsLinks
Notebooks for reproducing the paper "Computer Vision with a Single (Robust) Classifier"
☆128Updated 6 years ago
Alternatives and similar repositories for robustness_applications
Users that are interested in robustness_applications are comparing it to the libraries listed below
Sorting:
- Code for "Learning Perceptually-Aligned Representations via Adversarial Robustness"☆162Updated 5 years ago
- Code for "Testing Robustness Against Unforeseen Adversaries"☆80Updated last year
- Code for "Robustness May Be at Odds with Accuracy"☆91Updated 2 years ago
- Provably defending pretrained classifiers including the Azure, Google, AWS, and Clarifai APIs☆97Updated 4 years ago
- Datasets for the paper "Adversarial Examples are not Bugs, They Are Features"☆186Updated 5 years ago
- ☆88Updated last year
- Code for our NeurIPS 2019 *spotlight* "Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers"☆228Updated 5 years ago
- Investigating the robustness of state-of-the-art CNN architectures to simple spatial transformations.☆49Updated 6 years ago
- Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)☆100Updated 3 years ago
- Official repository for "Bridging Adversarial Robustness and Gradient Interpretability".☆30Updated 6 years ago
- Provable Robustness of ReLU networks via Maximization of Linear Regions [AISTATS 2019]☆31Updated 5 years ago
- Analysis of Adversarial Logit Pairing☆60Updated 7 years ago
- A PyTorch baseline attack example for the NIPS 2017 adversarial competition☆86Updated 8 years ago
- ☆30Updated 6 years ago
- Randomized Smoothing of All Shapes and Sizes (ICML 2020).☆51Updated 5 years ago
- ☆21Updated last year
- Data, code & materials from the paper "Generalisation in humans and deep neural networks" (NeurIPS 2018)☆96Updated 2 years ago
- Codebase for "Exploring the Landscape of Spatial Robustness" (ICML'19, https://arxiv.org/abs/1712.02779).☆26Updated 6 years ago
- SmoothGrad implementation in PyTorch☆172Updated 4 years ago
- Release of CIFAR-10.1, a new test set for CIFAR-10.☆224Updated 5 years ago
- A Closer Look at Accuracy vs. Robustness☆88Updated 4 years ago
- Official repository for our NeurIPS 2021 paper "Unadversarial Examples: Designing Objects for Robust Vision"☆105Updated last year
- Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks, in ICCV 2019☆58Updated 6 years ago
- A community-run reference for state-of-the-art adversarial example defenses.☆50Updated last year
- CVPR'19 experiments with (on-manifold) adversarial examples.☆45Updated 5 years ago
- ☆31Updated 5 years ago
- Code for the paper "Adversarial Training and Robustness for Multiple Perturbations", NeurIPS 2019☆47Updated 2 years ago
- Accompanying code for the paper "Zero-shot Knowledge Transfer via Adversarial Belief Matching"☆143Updated 5 years ago
- Trained model weights, training and evaluation code from the paper "A simple way to make neural networks robust against diverse image cor…☆62Updated 2 years ago
- Code for our nips19 paper: You Only Propagate Once: Accelerating Adversarial Training Via Maximal Principle☆178Updated last year