Out-of-distribution detection seeks to identify novelties, samples that deviate from the norm. The task has been found to be quite challenging, particularly in the case where the normal data distribution consist of multiple semantic classes (e.g. multiple object categories). To overcome this challenge, current approaches require manual labeling of the normal images provided during training. In this work, we tackle multi-class novelty detection without class labels. Our simple but effective solution consists of two stages: we first discover “pseudo-class” labels using unsupervised clustering. Then using these pseudo-class labels, we are able to use standard supervised out-of-distribution detection methods. We verify the performance of our method by favorable comparison to the state-of-the-art, and provide extensive analysis and ablations.
|Original language||American English|
|Title of host publication||Computer Vision – ECCV 2022 Workshops, Proceedings|
|Editors||Leonid Karlinsky, Tomer Michaeli, Ko Nishino|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||17|
|State||Published - 2023|
|Event||17th European Conference on Computer Vision, ECCV 2022 - Tel Aviv, Israel|
Duration: 23 Oct 2022 → 27 Oct 2022
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||17th European Conference on Computer Vision, ECCV 2022|
|Period||23/10/22 → 27/10/22|
Bibliographical noteFunding Information:
Acknowledgements. This work was partly supported by the Malvina and Solomon Pollack scholarship and, the Federmann Cyber Security Research Center in conjunction with the Israel National Cyber Directorate.
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.