Learning ODIN
Amir Jevnisek (Tel-Aviv University),* Shai Avidan (Tel Aviv University)
The 33rd British Machine Vision Conference
Abstract
ODIN is a popular Out-Of-Distribution (OOD) detection algorithm. It is based on the observation that using temperature scaling and adding small perturbations to the input can separate the softmax score distributions between in- and out-of-distribution images, allowing for more effective detection. Instead of passively making this observation, we derive a new loss, termed Gradient Quotient (GQ) loss, that encourages this behaviour by the network. GQ can be used either to train a classification network from scratch, or fine-tune it. We show theoretically why GQ encourages the observation made by ODIN and evaluate GQ on a number of network architectures and datasets. Experiments show that we achieve SOTA on a large number of standard benchmarks.Video
Citation
@inproceedings{Jevnisek_2022_BMVC,
author = {Amir Jevnisek and Shai Avidan},
title = {Learning ODIN},
booktitle = {33rd British Machine Vision Conference 2022, {BMVC} 2022, London, UK, November 21-24, 2022},
publisher = {{BMVA} Press},
year = {2022},
url = {https://bmvc2022.mpi-inf.mpg.de/0210.pdf}
}
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