GR Machado, E Silva, RR Goldschmidt - ACM Computing Surveys …, 2021 - dl.acm.org
Deep Learning algorithms have achieved state-of-the-art performance for Image Classification. For this reason, they have been used even in security-critical applications …
Q Xie, MT Luong, E Hovy… - Proceedings of the IEEE …, 2020 - openaccess.thecvf.com
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 …
A Ilyas, S Santurkar, D Tsipras… - Advances in neural …, 2019 - proceedings.neurips.cc
Adversarial examples have attracted significant attention in machine learning, but the reasons for their existence and pervasiveness remain unclear. We demonstrate that …
S Bubeck, M Sellke - Advances in Neural Information …, 2021 - proceedings.neurips.cc
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 …
X Yuan, P He, Q Zhu, X Li - IEEE transactions on neural …, 2019 - ieeexplore.ieee.org
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 …