BriefGPT.xyz
May, 2019
学习不完整数据时对抗性扰动的鲁棒性
Robustness to Adversarial Perturbations in Learning from Incomplete Data
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Amir Najafi, Shin-ichi Maeda, Masanori Koyama, Takeru Miyato
TL;DR
本文研究了在对抗性扰动的假设下，无标记数据在推断问题中的作用，并将两种主要的学习框架——半监督学习（SSL）和分布式鲁棒学习（DRL）统一起来，并在新的复杂性度量基础上进行了一般化理论的构建。
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 learni
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