@inproceedings{929489836377442a91fd9be42eb99b49,
title = "Anomaly Detection Requires Better Representations",
abstract = "Anomaly detection seeks to identify unusual phenomena, a central task in science and industry. The task is inherently unsupervised as anomalies are unexpected and unknown during training. Recent advances in self-supervised representation learning have directly driven improvements in anomaly detection. In this position paper, we first explain how self-supervised representations can be easily used to achieve state-of-the-art performance in commonly reported anomaly detection benchmarks. We then argue that tackling the next generation of anomaly detection tasks requires new technical and conceptual improvements in representation learning.",
keywords = "Anomaly detection, Representation learning, Self-Supervised learning",
author = "Tal Reiss and Niv Cohen and Eliahu Horwitz and Ron Abutbul and Yedid Hoshen",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 17th European Conference on Computer Vision, ECCV 2022 ; Conference date: 23-10-2022 Through 27-10-2022",
year = "2023",
doi = "10.1007/978-3-031-25069-9\_4",
language = "الإنجليزيّة",
isbn = "9783031250682",
series = "Lecture Notes in Computer Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "56--68",
editor = "Leonid Karlinsky and Tomer Michaeli and Ko Nishino",
booktitle = "Computer Vision – ECCV 2022 Workshops, Proceedings",
address = "ألمانيا",
}