Optimism in the Face of Adversity: Understanding and Improving Deep Learning Through Adversarial Robustness
METADATA ONLY
Loading...
Author / Producer
Date
2021-05
Publication Type
Journal Article
ETH Bibliography
yes
Citations
Altmetric
METADATA ONLY
Data
Rights / License
Abstract
Driven by massive amounts of data and important advances in computational resources, new deep learning systems have achieved outstanding results in a large spectrum of applications. Nevertheless, our current theoretical understanding of the mathematical foundations of deep learning lags far behind its empirical success. However, the field of adversarial robustness has recently become one of the main sources of explanations of our deep models. In this article, we provide an in-depth review of the field and give a self-contained introduction to its main notions. However, in contrast to the mainstream pessimistic perspective of adversarial robustness, we focus on the main positive aspects that it entails. We highlight the intuitive connection between adversarial examples and the geometry of deep neural networks and, eventually, explore how the geometric study of adversarial examples can serve as a powerful tool to understand deep learning. Furthermore, we demonstrate the broad applicability of adversarial robustness, providing an overview of the main emerging applications of adversarial robustness beyond security. The goal of this article is to provide readers with a set of new perspectives to understand deep learning and supply them with intuitive tools and insights on how to use adversarial robustness to improve it.
Permanent link
Publication status
published
External links
Editor
Book title
Journal / series
Volume
109 (5)
Pages / Article No.
635 - 659
Publisher
IEEE
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Adversarial robustness; deep learning; generalization; geometric analysis of neural networks; interpretability; neural networks; robustness to distribution shifts; transfer learning