With the development of Internet of Things (IoT), 5 G, and cloud computing technologies, the
amount of data from manufacturing systems has been increasing rapidly. With massive …

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 …

We present a simple self-training method that achieves 88.4% top-1 accuracy on ImageNet,
which is 2.0% better than the state-of-the-art model that requires 3.5 B weakly labeled …

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

Classically, data interpolation with a parametrized model class is possible as long as the
number of parameters is larger than the number of equations to be satisfied. A puzzling …

We demonstrate, theoretically and empirically, that adversarial robustness can significantly
benefit from semisupervised learning. Theoretically, we revisit the simple Gaussian model of …

With the rapid developments of artificial intelligence (AI) and deep learning (DL) techniques,
it is critical to ensure the security and robustness of the deployed algorithms. Recently, the …

In this paper we establish rigorous benchmarks for image classifier robustness. Our first
benchmark, ImageNet-C, standardizes and expands the corruption robustness topic, while …

We show that there may exist an inherent tension between the goal of adversarial
robustness and that of standard generalization. Specifically, training robust models may not …

With rapid progress and significant successes in a wide spectrum of applications, deep
learning is being applied in many safety-critical environments. However, deep neural …