Out of Distribution Detection on ImageNet-O

• URL: http://arxiv.org/abs/2201.09352v1
• Date: Sun, 23 Jan 2022 20:02:08 GMT
• Title: Out of Distribution Detection on ImageNet-O
• Authors: Anugya Srivastava, Shriya Jain and Mugdha Thigle
• Abstract summary: Out of distribution (OOD) detection is a crucial part of making machine learning systems robust. The ImageNet-O dataset is an important tool in testing the robustness of ImageNet trained deep neural networks. We aim to perform a comparative analysis of OOD detection methods on ImageNet-O.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Out of distribution (OOD) detection is a crucial part of making machine learning systems robust. The ImageNet-O dataset is an important tool in testing the robustness of ImageNet trained deep neural networks that are widely used across a variety of systems and applications. We aim to perform a comparative analysis of OOD detection methods on ImageNet-O, a first of its kind dataset with a label distribution different than that of ImageNet, that has been created to aid research in OOD detection for ImageNet models. As this dataset is fairly new, we aim to provide a comprehensive benchmarking of some of the current state of the art OOD detection methods on this novel dataset. This benchmarking covers a variety of model architectures, settings where we haves prior access to the OOD data versus when we don't, predictive score based approaches, deep generative approaches to OOD detection, and more.

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