VITA-Group / Alleviate-Robust-Overfitting
[ICLR 2021] "Robust Overfitting may be mitigated by properly learned smoothening" by Tianlong Chen*, Zhenyu Zhang*, Sijia Liu, Shiyu Chang, Zhangyang Wang
☆46Updated 2 years ago
Related projects ⓘ
Alternatives and complementary repositories for Alleviate-Robust-Overfitting
- the paper "Geometry-aware Instance-reweighted Adversarial Training" ICLR 2021 oral☆57Updated 3 years ago
- Pytorch implementation of Adversarially Robust Distillation (ARD)☆59Updated 5 years ago
- ☆35Updated 3 years ago
- On the effectiveness of adversarial training against common corruptions [UAI 2022]☆30Updated 2 years ago
- Learnable Boundary Guided Adversarial Training (ICCV2021)☆34Updated last year
- Unofficial implementation of the DeepMind papers "Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples…☆92Updated 2 years ago
- Helper-based Adversarial Training: Reducing Excessive Margin to Achieve a Better Accuracy vs. Robustness Trade-off☆29Updated 2 years ago
- Code for the paper "A Light Recipe to Train Robust Vision Transformers" [SaTML 2023]☆52Updated last year
- Fighting Gradients with Gradients: Dynamic Defenses against Adversarial Attacks☆37Updated 3 years ago
- Implementation for <Understanding Robust Overftting of Adversarial Training and Beyond> in ICML'22.☆12Updated 2 years ago
- Understanding and Improving Fast Adversarial Training [NeurIPS 2020]☆94Updated 3 years ago
- ☆13Updated 4 years ago
- Implementation of Wasserstein adversarial attacks.☆22Updated 3 years ago
- [NeurIPS 2020] code for "Boundary thickness and robustness in learning models"☆18Updated 3 years ago
- [NeurIPS2021] Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks☆34Updated 4 months ago
- Code and data for the ICLR 2021 paper "Perceptual Adversarial Robustness: Defense Against Unseen Threat Models".☆54Updated 2 years ago
- Adversarial Distributional Training (NeurIPS 2020)☆61Updated 3 years ago
- CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection☆20Updated 3 years ago
- [NeurIPS 2021] “When does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning?”☆46Updated 3 years ago
- One-Pixel Shortcut: on the Learning Preference of Deep Neural Networks (ICLR 2023 Spotlight)☆12Updated last year
- Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses, NeurIPS Spotlight 2020☆24Updated 3 years ago
- Implementation for <Robust Weight Perturbation for Adversarial Training> in IJCAI'22.☆14Updated 2 years ago
- Adversarial Defense for Ensemble Models (ICML 2019)☆61Updated 3 years ago
- Code for the CVPR 2020 article "Adversarial Vertex mixup: Toward Better Adversarially Robust Generalization"☆13Updated 4 years ago
- Code for our NeurIPS 2020 paper Backpropagating Linearly Improves Transferability of Adversarial Examples.☆42Updated last year
- Code for the paper "MMA Training: Direct Input Space Margin Maximization through Adversarial Training"☆34Updated 4 years ago
- ☆55Updated 2 years ago
- A Self-Consistent Robust Error (ICML 2022)☆67Updated last year
- Feature Scattering Adversarial Training (NeurIPS19)☆71Updated 5 months ago
- Imbalanced Gradients: A New Cause of Overestimated Adversarial Robustness. (MD attacks)☆11Updated 4 years ago