Out of distribution (OOD) detection is a crucial part of making machine learning systems robust. The is an important tool in testing the robustness of ImageNet [4] trained deep neural networks that are widely used across a variety of systems and applications. Inspired by [27], we aim to perform a comparative analysis of OOD detection methods on ImageNet-O [9], a first of its kind dataset with a label distribution different than that of Im-ageNet, that has been created to aid research in OOD detection for ImageNet [4] 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. The code is available here.
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