Dan Hendrycks

UC Berkeley
danhendrycks.com
Publications36
h-index23
Citations6,805
Highly Influential Citations1,346
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  • Publications
  • Influence
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
TLDR
A simple baseline that utilizes probabilities from softmax distributions is presented, showing the effectiveness of this baseline across all computer vision, natural language processing, and automatic speech recognition, and it is shown the baseline can sometimes be surpassed.
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
TLDR
This paper standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications, and proposes a new dataset called ImageNet-P which enables researchers to benchmark a classifier's robustness to common perturbations.
Deep Anomaly Detection with Outlier Exposure
TLDR
In extensive experiments on natural language processing and small- and large-scale vision tasks, it is found that Outlier Exposure significantly improves detection performance and that cutting-edge generative models trained on CIFar-10 may assign higher likelihoods to SVHN images than to CIFAR-10 images; OE is used to mitigate this issue.
Gaussian Error Linear Units (GELUs)
TLDR
An empirical evaluation of the GELU nonlinearity against the ReLU and ELU activations is performed and performance improvements are found across all considered computer vision, natural language processing, and speech tasks.
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
TLDR
AugMix significantly improves robustness and uncertainty measures on challenging image classification benchmarks, closing the gap between previous methods and the best possible performance in some cases by more than half.
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
TLDR
This work finds that self-supervision can benefit robustness in a variety of ways, including robustness to adversarial examples, label corruption, and common input corruptions, and greatly benefits out-of-distribution detection on difficult, near-dist distribution outliers.
The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization
TLDR
It is found that using larger models and artificial data augmentations can improve robustness on real-world distribution shifts, contrary to claims in prior work.
Natural Adversarial Examples
TLDR
This work introduces two challenging datasets that reliably cause machine learning model performance to substantially degrade and curates an adversarial out-of-distribution detection dataset called IMAGENET-O, which is the first out- of-dist distribution detection dataset created for ImageNet models.
Bridging Nonlinearities and Stochastic Regularizers with Gaussian Error Linear Units
TLDR
An empirical evaluation of the GELU nonlinearity against the ReLU and ELU activations and finding performance improvements across all tasks suggests a new probabilistic understanding of nonlinearities.
Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise
TLDR
It is demonstrated that robustness to label noise up to severe strengths can be achieved by using a set of trusted data with clean labels, and a loss correction that utilizes trusted examples in a data-efficient manner to mitigate the effects of label noise on deep neural network classifiers is proposed.
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