IAM

21thJULY2018

READING

Ruitong Huang, Bing Xu, Dale Schuurmans, Csaba Szepesvári. Learning with a Strong Adversary. CoRR abs/1511.03034, 2015.

Huang et al. propose a variant of adversarial training called “learning with a strong adversary”. In spirit the idea is also similar to related work [1]. In particular, the authors consider the min-max objective

$\min_g \sum_i \max_{\|r^{(i)}\|\leq c} l(g(x_i + r^{(i)}), y_i)$

where $g$ ranges over expressible functions and $(x_i, y_i)$ is a training sample. In the remainder of the paper, Huang et al. Address the problem of efficiently computing $r^{(i)}$ – i.e. a strong adversarial example based on the current state of the network – and subsequently updating the weights of the network by computing the gradient of the augmented loss. Details can be found in the paper.

  • [1] T. Miyato, S. Maeda, M. Koyama, K. Nakae, S. Ishii. Distributional Smoothing by Virtual Adversarial Training. ArXiv:1507.00677, 2015.
Also find this summary on ShortScience.org.

What is your opinion on the summarized work? Or do you know related work that is of interest? Let me know your thoughts in the comments below: