Dan Hendrycks

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berkeley college ∙ Toyota Technological Institute at Chicago

Graduate Student at UC Berkeley

Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

In this paper we establish rigorous benchmarks for image classifier robu...
03/28/2019 ∙ by Dan Hendrycks, et al. ∙ 30 ∙ share

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A Benchmark for Anomaly Segmentation

Detecting out-of-distribution examples is important for safety-critical ...
11/25/2019 ∙ by Dan Hendrycks, et al. ∙ 26 ∙ share

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AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty

Modern deep neural networks can achieve high accuracy when the training ...
12/05/2019 ∙ by Dan Hendrycks, et al. ∙ 21 ∙ share

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Using Pre-Training Can Improve Model Robustness and Uncertainty

Tuning a pre-trained network is commonly thought to improve data efficie...
01/28/2019 ∙ by Dan Hendrycks, et al. ∙ 16 ∙ share

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Transfer of Adversarial Robustness Between Perturbation Types

We study the transfer of adversarial robustness of deep neural networks ...
05/03/2019 ∙ by Daniel Kang, et al. ∙ 12 ∙ share

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Deep Anomaly Detection with Outlier Exposure

It is important to detect and handle anomalous inputs when deploying mac...
12/11/2018 ∙ by Dan Hendrycks, et al. ∙ 8 ∙ share

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Natural Adversarial Examples

We introduce natural adversarial examples -- real-world, unmodified, and...
07/16/2019 ∙ by Dan Hendrycks, et al. ∙ 6 ∙ share

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Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty

Self-supervision provides effective representations for downstream tasks...
06/28/2019 ∙ by Dan Hendrycks, et al. ∙ 5 ∙ share

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Open Category Detection with PAC Guarantees

Open category detection is the problem of detecting "alien" test instanc...
08/01/2018 ∙ by Si Liu, et al. ∙ 4 ∙ share

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Testing Robustness Against Unforeseen Adversaries

Considerable work on adversarial defense has studied robustness to a fix...
08/21/2019 ∙ by Daniel Kang, et al. ∙ 1 ∙ share

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Adjusting for Dropout Variance in Batch Normalization and Weight Initialization

We show how to adjust for the variance introduced by dropout with correc...
07/08/2016 ∙ by Dan Hendrycks, et al. ∙ 0 ∙ share

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A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

We consider the two related problems of detecting if an example is miscl...
10/07/2016 ∙ by Dan Hendrycks, et al. ∙ 0 ∙ share

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Early Methods for Detecting Adversarial Images

Many machine learning classifiers are vulnerable to adversarial perturba...
08/01/2016 ∙ by Dan Hendrycks, et al. ∙ 0 ∙ share

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Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise

The growing importance of massive datasets with the advent of deep learn...
02/14/2018 ∙ by Dan Hendrycks, et al. ∙ 0 ∙ share

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Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations

In this paper we establish rigorous benchmarks for image classifier robu...
07/04/2018 ∙ by Dan Hendrycks, et al. ∙ 0 ∙ share

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Dan Hendrycks

Featured Co-authors

Dawn Song

Kevin Gimpel

Balaji Lakshminarayanan

Yi Sun

Si Liu

Barret Zoph

Jacob Steinhardt

Alan Fern

Thomas G. Dietterich

Justin Gilmer

Ekin D. Cubuk

Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

A Benchmark for Anomaly Segmentation

AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty

Using Pre-Training Can Improve Model Robustness and Uncertainty

Transfer of Adversarial Robustness Between Perturbation Types

Deep Anomaly Detection with Outlier Exposure

Natural Adversarial Examples

Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty

Open Category Detection with PAC Guarantees

Testing Robustness Against Unforeseen Adversaries

Adjusting for Dropout Variance in Batch Normalization and Weight Initialization

A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

Early Methods for Detecting Adversarial Images

Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise

Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations