epoch用多少,应该按weight\theta想要迭代多少步来计算,大致地, 只要算法2训练\theta的步数和 原始的PGD-based adv. training (算法1) 训练\theta的步数一样的步数一样,就能获得和算法1相似的adversarial robustness. the scalability(size) of networks 大一些能够提升一点 adversarial robustness, 同时也会花更长的训...
Our "free" adversarial training algorithm achieves comparable robustness to PGD adversarial training on the CIFAR-10 and CIFAR-100 datasets at negligible additional cost compared to natural training, and can be 7 to 30 times faster than other strong adversarial training methods. Using a single work...
Official TensorFlow Implementation of Adversarial Training for Free! which trains robust models at no extra cost compared to natural training. attack classification robust adversarial robustness adversarial-learning attack-defense adversarial-machine-learning adversarial-example adversarial-examples adversarial-attack...
To prevent potential overadaptation of new defenses to AutoAttack, we also welcome external evaluations based on adaptive attacks, especially where AutoAttack flags a potential overestimation of robustness. For each model, we are interested in the best known robust accuracy and see AutoAttack and ...
Huang S, Lu Z, Deb K, Boddeti VN (2023) Revisiting residual networks for adversarial robustness. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 8202–8211 Ghosh S, Chatterjee A (2023) Transfer-ensemble learning based deep convolutional neural networks ...
To prevent potential overadaptation of new defenses to AutoAttack, we also welcome external evaluations based on adaptive attacks, especially where AutoAttack flags a potential overestimation of robustness. For each model, we are interested in the best known robust accuracy and see AutoAttack and ...
This strategy effectively balances the requirements of the domain alignment and the class alignment, thus preventing situations where one task achieves a high accuracy while the other underperforms, thereby further enhancing the model's overall performance and transfer robustness. The code and datasets ...
Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free 来自 arXiv.org 喜欢 0 阅读量: 149 作者:H Wang,T Chen,S Gui,TK Hu,J Liu,Z Wang 摘要: Adversarial training and its many variants substantially improve deep network robustness, yet at the cost of ...
For example, the adversarial robustness ofΔCLIP surpasses that of the previous best models on ImageNet-1k by ~20%. Paper Add Code Towards a constructive framework for control theory no code yet •4 Jan 2025 Such observations indicate that computational uncertainty should indeed be addressed expl...
Our "free" adversarial training algorithm achieves comparable robustness to PGD adversarial training on the CIFAR-10 and CIFAR-100 datasets at negligible additional cost compared to natural training, and can be 7 to 30 times faster than other strong adversarial training methods. Using a single work...