Adversarially Robust Generalization Requires More Data
Adversarially Robust Generalization Requires More Data
Schmidt, Ludwig and Santurkar, Shibani and Tsipras, Dimitris and Talwar, Kunal and Madry, Aleksander
2018

Paper summary
davidstutz
Schmidt et al. theoretically and experimentally show that training adversarially robust models requires a higher sample complexity compared to regular generalization. Theoretically, they analyze two very simple families of datasets, e.g., consisting of two Gaussian distributions corresponding to a two-class problem. On such datasets, they proof that “robust generalization”, i.e., generalization to adversarial examples, required much higher sample complexity compared to regular generlization, i.e., generalization to the test set. These results are interesting because they suggest that the sample complexity might be even worse for more complex and realistic data distributions – as we commonly tackle in computer vision.
Experimentally, they show similar result son MNIST, CIFAR-10 and SVHN. Varying the size of the training set and plotting the accuracy on adversarially computed examples results in Figure 1. As can be seen, there seems to be a clear advantage of having larger training sets. Note that these models were trained using adversarial training using an $L_\infty$ adversary constrained by the given $\epsilon$.
https://i.imgur.com/SriBAt4.png
Figure 1: Training set size plotted against the adversarial test accuracy on MNIST, CIFAR-10 and SVHN. The models were trained using adversarial training.
Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).
Adversarially Robust Generalization Requires More Data

Schmidt, Ludwig and Santurkar, Shibani and Tsipras, Dimitris and Talwar, Kunal and Madry, Aleksander

Neural Information Processing Systems Conference - 2018 via Local Bibsonomy

Keywords: dblp

Schmidt, Ludwig and Santurkar, Shibani and Tsipras, Dimitris and Talwar, Kunal and Madry, Aleksander

Neural Information Processing Systems Conference - 2018 via Local Bibsonomy

Keywords: dblp

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