Robustness to Adversarial Perturbations in Learning from Incomplete Data

Amir Najafi; Shin-ichi Maeda; Masanori Koyama; Takeru Miyato

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arXiv · 1905.13021

Robustness to Adversarial Perturbations in Learning from Incomplete Data

Amir Najafi·Shin-ichi Maeda·Masanori Koyama·Takeru Miyato

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Abstract

What is the role of unlabeled data in an inference problem, when the presumed underlying distribution is adversarially perturbed? To provide a concrete answer to this question, this paper unifies two major learning frameworks: Semi-Supervised Learning (SSL) and Distributionally Robust Learning (DRL). We develop a generalization theory for our framework based on a number of novel complexity measures, such as an adversarial extension of Rademacher complexity and its semi-supervised analogue. Moreover, our analysis is able to quantify the role of unlabeled data in the generalization under a more general condition compared to the existing theoretical works in SSL. Based on our framework, we also present a hybrid of DRL and EM algorithms that has a guaranteed convergence rate. When implemented with deep neural networks, our method shows a comparable performance to those of the state-of-the-art on a number of real-world benchmark datasets.

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Amir Najafi, Shin-ichi Maeda, Masanori Koyama, Takeru Miyato. 2019-05-24. Robustness to Adversarial Perturbations in Learning from Incomplete Data. https://arxiv.org/abs/1905.13021

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