402 citations · 845 across the 4 of their papers we have counts for
4 papers
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
Dan Hendrycks, Thomas Dietterich
In this paper we establish rigorous benchmarks for image classifier robustness. Our first benchmark, ImageNet-C, standardizes and expands the corruption robustness topic, while sho…
Deep Anomaly Detection with Outlier Exposure
Dan Hendrycks, Mantas Mazeika, Thomas Dietterich
It is important to detect anomalous inputs when deploying machine learning systems. The use of larger and more complex inputs in deep learning magnifies the difficulty of distingui…
Early Methods for Detecting Adversarial Images
Dan Hendrycks, Kevin Gimpel
Many machine learning classifiers are vulnerable to adversarial perturbations. An adversarial perturbation modifies an input to change a classifier's prediction without causing the…
Adjusting for Dropout Variance in Batch Normalization and Weight Initialization
Dan Hendrycks, Kevin Gimpel
We show how to adjust for the variance introduced by dropout with corrections to weight initialization and Batch Normalization, yielding higher accuracy. Though dropout can preserv…