microsoft / unadversarial
Official repository for our NeurIPS 2021 paper "Unadversarial Examples: Designing Objects for Robust Vision"
☆104Updated 8 months ago
Alternatives and similar repositories for unadversarial:
Users that are interested in unadversarial are comparing it to the libraries listed below
- Provably defending pretrained classifiers including the Azure, Google, AWS, and Clarifai APIs☆97Updated 4 years ago
- A Closer Look at Accuracy vs. Robustness☆88Updated 3 years ago
- Codebase for "Exploring the Landscape of Spatial Robustness" (ICML'19, https://arxiv.org/abs/1712.02779).☆26Updated 5 years ago
- Understanding and Improving Fast Adversarial Training [NeurIPS 2020]☆96Updated 3 years ago
- Smooth Adversarial Training☆67Updated 4 years ago
- Code and data for the ICLR 2021 paper "Perceptual Adversarial Robustness: Defense Against Unseen Threat Models".☆55Updated 3 years ago
- ☆54Updated 4 years ago
- PRIME: A Few Primitives Can Boost Robustness to Common Corruptions☆42Updated 2 years ago
- On the effectiveness of adversarial training against common corruptions [UAI 2022]☆30Updated 2 years ago
- A framework for analyzing computer vision models with simulated data☆125Updated 3 years ago
- Notebooks for reproducing the paper "Computer Vision with a Single (Robust) Classifier"☆128Updated 5 years ago
- ☆87Updated 8 months ago
- ☆140Updated 4 years ago
- Code for "Testing Robustness Against Unforeseen Adversaries"☆81Updated 8 months ago
- [ICML'20] Multi Steepest Descent (MSD) for robustness against the union of multiple perturbation models.☆26Updated 8 months ago
- Investigating the robustness of state-of-the-art CNN architectures to simple spatial transformations.☆49Updated 5 years ago
- Code for the paper "Understanding Generalization through Visualizations"☆60Updated 4 years ago
- Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks, in ICCV 2019☆59Updated 5 years ago
- Datasets for the paper "Adversarial Examples are not Bugs, They Are Features"☆187Updated 4 years ago
- Official repo for the paper "Make Some Noise: Reliable and Efficient Single-Step Adversarial Training" (https://arxiv.org/abs/2202.01181)☆25Updated 2 years ago
- Source code of "Hold me tight! Influence of discriminative features on deep network boundaries"☆22Updated 3 years ago
- ImageNet Testbed, associated with the paper "Measuring Robustness to Natural Distribution Shifts in Image Classification."☆118Updated last year
- Code for the paper "Consistency Regularization for Certified Robustness of Smoothed Classifiers" (NeurIPS 2020)☆35Updated 4 years ago
- Source code for "Neural Anisotropy Directions"☆15Updated 4 years ago
- Implementation of Confidence-Calibrated Adversarial Training (CCAT).☆45Updated 4 years ago
- ICLR 2021, Fair Mixup: Fairness via Interpolation☆55Updated 3 years ago
- ☆39Updated last year
- Official implementation for paper: A New Defense Against Adversarial Images: Turning a Weakness into a Strength☆38Updated 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…☆61Updated last year
- Randomized Smoothing of All Shapes and Sizes (ICML 2020).☆52Updated 4 years ago