Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness

Understand

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 on the mathematical foundations of deep learning lags far behind its empirical success.
• Towards solving the vulnerability of neural networks, 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 of adversarial robustness in deep learning, and give a self-contained introduction to its main notions.