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.
@article{arxiv.2208.00629,
title = {XOOD: Extreme Value Based Out-Of-Distribution Detection For Image Classification},
author = {Frej Berglind and Haron Temam and Supratik Mukhopadhyay and Kamalika Das and Md Saiful Islam Sajol and Sricharan Kumar and Kumar Kallurupalli},
journal= {arXiv preprint arXiv:2208.00629},
year = {2022}
}