Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

13 code implementations • ICLR 2019 • Dan Hendrycks, Thomas Dietterich

Then we propose a new dataset called ImageNet-P which enables researchers to benchmark a classifier's robustness to common perturbations.

Adversarial Defense Benchmarking +1

Deep Anomaly Detection with Outlier Exposure

9 code implementations • ICLR 2019 • Dan Hendrycks, Mantas Mazeika, Thomas Dietterich

We also analyze the flexibility and robustness of Outlier Exposure, and identify characteristics of the auxiliary dataset that improve performance.

Anomaly Detection Out-of-Distribution Detection +1

A Meta-Analysis of the Anomaly Detection Problem

1 code implementation • 3 Mar 2015 • Andrew Emmott, Shubhomoy Das, Thomas Dietterich, Alan Fern, Weng-Keen Wong

The intended contributions of this article are many; in addition to providing a large publicly-available corpus of anomaly detection benchmarks, we provide an ontology for describing anomaly detection contexts, a methodology for controlling various aspects of benchmark creation, guidelines for future experimental design and a discussion of the many potential pitfalls of trying to measure success in this field.

Anomaly Detection Benchmarking +2

A Conditional Multinomial Mixture Model for Superset Label Learning

1 code implementation • 12 2012 • LiPing Liu, Thomas Dietterich

We propose a probabilistic model, the Logistic StickBreaking Conditional Multinomial Model (LSB-CMM), to do the job.