schzhu / learning-adversarially-robust-representations
Code for the paper: Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization (https://arxiv.org/abs/2002.11798)
☆22Updated 4 years ago
Alternatives and similar repositories for learning-adversarially-robust-representations:
Users that are interested in learning-adversarially-robust-representations are comparing it to the libraries listed below
- On the effectiveness of adversarial training against common corruptions [UAI 2022]☆30Updated 2 years ago
- ☆35Updated 4 years ago
- Pytorch implementation of Adversarially Robust Distillation (ARD)☆59Updated 5 years ago
- Further improve robustness of mixup-trained models in inference (ICLR 2020)☆60Updated 4 years ago
- Understanding and Improving Fast Adversarial Training [NeurIPS 2020]☆96Updated 3 years ago
- Adversarial Defense for Ensemble Models (ICML 2019)☆61Updated 4 years ago
- Code for the paper "MMA Training: Direct Input Space Margin Maximization through Adversarial Training"☆34Updated 5 years ago
- [ICLR 2021] "Robust Overfitting may be mitigated by properly learned smoothening" by Tianlong Chen*, Zhenyu Zhang*, Sijia Liu, Shiyu Chan…☆46Updated 3 years ago
- Code for the paper "Adversarial Training and Robustness for Multiple Perturbations", NeurIPS 2019☆47Updated 2 years ago
- [ICML'20] Multi Steepest Descent (MSD) for robustness against the union of multiple perturbation models.☆26Updated 8 months ago
- Implementation for Jacobian Adversarially Regularized Networks for Robustness (ICLR 2020)☆21Updated 5 years ago
- Code release for the ICML 2019 paper "Are generative classifiers more robust to adversarial attacks?"☆23Updated 5 years ago
- Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses, NeurIPS Spotlight 2020☆27Updated 4 years ago
- Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)☆100Updated 3 years ago
- On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them [NeurIPS 2020]☆36Updated 3 years ago
- ☆8Updated 4 years ago
- Max Mahalanobis Training (ICML 2018 + ICLR 2020)☆90Updated 4 years ago
- Codes for our ICLR2020 paper: Knowledge Consistency between Neural Networks and Beyond☆16Updated 5 years ago
- Code and data for the ICLR 2021 paper "Perceptual Adversarial Robustness: Defense Against Unseen Threat Models".☆55Updated 3 years ago
- CVPR'19 experiments with (on-manifold) adversarial examples.☆44Updated 5 years ago
- A PyTorch implementation of the method found in "Adversarially Robust Few-Shot Learning: A Meta-Learning Approach"☆50Updated 4 years ago
- ☆14Updated 6 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
- Code implementing the experiments described in the NeurIPS 2018 paper "With Friends Like These, Who Needs Adversaries?".☆13Updated 4 years ago
- Codebase for "Exploring the Landscape of Spatial Robustness" (ICML'19, https://arxiv.org/abs/1712.02779).☆26Updated 5 years ago
- Feature Scattering Adversarial Training (NeurIPS19)☆73Updated 10 months ago
- Provable Robustness of ReLU networks via Maximization of Linear Regions [AISTATS 2019]☆32Updated 4 years ago
- Learnable Boundary Guided Adversarial Training (ICCV2021)☆38Updated 4 months ago
- [NeurIPS 2020] code for "Boundary thickness and robustness in learning models"☆19Updated 4 years ago
- ☆11Updated 2 months ago