MadryLab / spatial-pytorchLinks
Codebase for "Exploring the Landscape of Spatial Robustness" (ICML'19, https://arxiv.org/abs/1712.02779).
☆26Updated 5 years ago
Alternatives and similar repositories for spatial-pytorch
Users that are interested in spatial-pytorch are comparing it to the libraries listed below
Sorting:
- Project page for our paper: Interpreting Adversarially Trained Convolutional Neural Networks☆66Updated 5 years ago
- Feature Scattering Adversarial Training (NeurIPS19)☆73Updated last year
- Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)☆100Updated 3 years ago
- A Closer Look at Accuracy vs. Robustness☆89Updated 4 years ago
- ☆35Updated 4 years ago
- Provably defending pretrained classifiers including the Azure, Google, AWS, and Clarifai APIs☆97Updated 4 years ago
- Smooth Adversarial Training☆68Updated 4 years ago
- Further improve robustness of mixup-trained models in inference (ICLR 2020)☆60Updated 5 years ago
- ☆88Updated last year
- Understanding and Improving Fast Adversarial Training [NeurIPS 2020]☆95Updated 3 years ago
- CVPR'19 experiments with (on-manifold) adversarial examples.☆45Updated 5 years ago
- Semisupervised learning for adversarial robustness https://arxiv.org/pdf/1905.13736.pdf☆142Updated 5 years ago
- Implementation of our NeurIPS 2019 paper: Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box Attacks☆10Updated 5 years ago
- Adversarial Defense for Ensemble Models (ICML 2019)☆61Updated 4 years ago
- Implementation of Confidence-Calibrated Adversarial Training (CCAT).☆45Updated 5 years ago
- [ICML'20] Multi Steepest Descent (MSD) for robustness against the union of multiple perturbation models.☆26Updated last year
- Coupling rejection strategy against adversarial attacks (CVPR 2022)☆29Updated 3 years ago
- Code for the paper "MMA Training: Direct Input Space Margin Maximization through Adversarial Training"☆34Updated 5 years ago
- ☆8Updated 4 years ago
- On the effectiveness of adversarial training against common corruptions [UAI 2022]☆30Updated 3 years ago
- Code for NeurIPS 2019 Paper☆47Updated 5 years ago
- Code for the paper "Adversarial Training and Robustness for Multiple Perturbations", NeurIPS 2019☆47Updated 2 years ago
- On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them [NeurIPS 2020]☆36Updated 4 years ago
- Towards Achieving Adversarial Robustness by Enforcing Feature Consistency Across Bit Planes☆23Updated 5 years ago
- RayS: A Ray Searching Method for Hard-label Adversarial Attack (KDD2020)☆56Updated 4 years ago
- Repository for our ICCV 2019 paper: Adversarial Defense via Learning to Generate Diverse Attacks☆22Updated 3 years ago
- ICLR 2021, Fair Mixup: Fairness via Interpolation☆56Updated 3 years ago
- Code for Stability Training with Noise (STN)☆22Updated 4 years ago
- Code for "Learning Perceptually-Aligned Representations via Adversarial Robustness"☆161Updated 5 years ago
- Source code for Learning Transferable Adversarial Examples via Ghost Networks (AAAI2020)☆57Updated 6 years ago