Universal Adversarial Training Universal Adversarial Training
Paper summary Shafahi et al. propose universal adversarial training, meaning training on universal adversarial examples. In contrast to regular adversarial examples, universal ones represent perturbations that cause a network to mis-classify many test images. In contrast to regular adversarial training, where several additional iterations are required on each batch of images, universal adversarial training only needs one additional forward/backward pass on each batch. The obtained perturbations for each batch are accumulated in a universal adversarial examples. This makes adversarial training more efficient, however reduces robustness significantly. Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).

Summary by David Stutz 1 year ago
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