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arXiv · 2208.00629

XOOD: Extreme Value Based Out-Of-Distribution Detection For Image Classification

Frej Berglind·Haron Temam·Supratik Mukhopadhyay·Kamalika Das·Md Saiful Islam Sajol·Sricharan Kumar·Kumar Kallurupalli
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Abstract

Detecting out-of-distribution (OOD) data at inference time is crucial for many applications of machine learning. We present XOOD: a novel extreme value-based OOD detection framework for image classification that consists of two algorithms. The first, XOOD-M, is completely unsupervised, while the second XOOD-L is self-supervised. Both algorithms rely on the signals captured by the extreme values of the data in the activation layers of the neural network in order to distinguish between in-distribution and OOD instances. We show experimentally that both XOOD-M and XOOD-L outperform state-of-the-art OOD detection methods on many benchmark data sets in both efficiency and accuracy, reducing false-positive rate (FPR95) by 50%, while improving the inferencing time by an order of magnitude.

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Frej Berglind, Haron Temam, Supratik Mukhopadhyay, Kamalika Das, Md Saiful Islam Sajol, Sricharan Kumar, Kumar Kallurupalli. 2022-08-01. XOOD: Extreme Value Based Out-Of-Distribution Detection For Image Classification. https://arxiv.org/abs/2208.00629

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