Deep Learning is the most widely used tool in the contemporary field of computer vision. Its
ability to accurately solve complex problems is employed in vision research to learn deep …

Deep neural networks (DNN) have achieved unprecedented success in numerous machine
learning tasks in various domains. However, the existence of adversarial examples raises …

Several works have proposed Simplicity Bias (SB)---the tendency of standard training
procedures such as Stochastic Gradient Descent (SGD) to find simple models---to justify why …

Adversarial examples have attracted significant attention in machine learning, but the
reasons for their existence and pervasiveness remain unclear. We demonstrate that …

Adversarial training, in which a network is trained on adversarial examples, is one of the few
defenses against adversarial attacks that withstands strong attacks. Unfortunately, the high …

We investigate the relationship between the frequency spectrum of image data and the
generalization behavior of convolutional neural networks (CNN). We first notice CNN's …

Deep reinforcement learning (RL) policies are known to be vulnerable to adversarial
perturbations to their observations, similar to adversarial examples for classifiers. However …

Deep learning is at the heart of the current rise of artificial intelligence. In the field of
computer vision, it has become the workhorse for applications ranging from self-driving cars …

Defenses against adversarial examples, such as adversarial training, are typically tailored to
a single perturbation type (eg, small $\ell_\infty $-noise). For other perturbations, these …

Deep Learning (DL) is vulnerable to out-of-distribution and adversarial examples resulting in
incorrect outputs. To make DL more robust, several posthoc (or runtime) anomaly detection …