ROBUST LEARNING AND PREDICTION IN DEEP LEARNING

Abstract

Robustness is the ability to withstand adverse conditions. When it is transposed into deep learning, it refers to the ability to tolerate perturbations that might affect the deep model's functionality. Learning a deep model and deploying it for usage require robustness. In this thesis, we explore two types of robustness in deep learning, i.e., training robustness and adversarial robustness. Training robustness refers to successfully learning a deep neural network under slight perturbations of the training configurations. Adversarial robustness refers to maintaining faithful predictions of the deep neural network even if the input data are perturbed by adversarially crafted noise.