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Implement the PGD-AT and TRADES for robust ResNet on CIFAR-10
AGAIN: Adversarial Training with Attribution Span Enlargement and Hybrid Feature Fusion
ATTA (Efficient Adversarial Training with Transferable Adversarial Examples)
[CVPR23] "Towards Compositional Adversarial Robustness: Generalizing Adversarial Training to Composite Semantic Perturbations" by Lei Hsiung, Yun-Yun Tsai, Pin-Yu Chen, and Tsung-Yi Ho.
Official code for "TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization", CVPR 2023
Code for the paper "Better Diffusion Models Further Improve Adversarial Training" (ICML 2023)
强化学习中文教程(蘑菇书),在线阅读地址:https://datawhalechina.github.io/easy-rl/
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger (ICML2020 Paper)
Feature Separation and Recalibration (CVPR 2023 Highlights)
CVPR 2023 generalist
Code for LAS-AT: Adversarial Training with Learnable Attack Strategy (CVPR2022)
Max Mahalanobis Training (ICML 2018 + ICLR 2020)
Code for the paper "MMA Training: Direct Input Space Margin Maximization through Adversarial Training"
This repository provides simple PyTorch implementations for adversarial training methods on CIFAR-10.
This repo contains the code used in the paper "Splitting the Difference on Adversarial Training" accepted to USENIX Security 24'
[CVPR 2022 oral] Subspace Adversarial Training
TRADES (TRadeoff-inspired Adversarial DEfense via Surrogate-loss minimization)
Unsupervised Cross-domain Adversarial Training
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