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

• URL: http://arxiv.org/abs/2208.00629v1
• Date: Mon, 1 Aug 2022 06:22:33 GMT
• Title: XOOD: Extreme Value Based Out-Of-Distribution Detection For Image Classification
• Authors: Frej Berglind, Haron Temam, Supratik Mukhopadhyay, Kamalika Das, Md Saiful Islam Sajol, Sricharan Kumar, Kumar Kallurupalli
• Abstract summary: We present XOOD: a novel extreme value-based OOD detection framework for image classification. Both algorithms rely on the signals captured by the extreme values of the data in the activation layers of the neural network. 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.
• Score: 1.1866955981399967
• License: http://creativecommons.org/licenses/by/4.0/
• 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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