Anomaly Detection Requires Better Representations

Tal Reiss; Niv Cohen; Eliahu Horwitz; Ron Abutbul; Yedid Hoshen

doi:10.1007/978-3-031-25069-9_4

Anomaly Detection Requires Better Representations

Tal Reiss^*
, Niv Cohen
, Eliahu Horwitz
, Ron Abutbul
, Yedid Hoshen

^*Corresponding author for this work

The Rachel and Selim Benin School of Engineering and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

19 Scopus citations

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.

Original language	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
Pages	56-68
Number of pages	13
ISBN (Print)	9783031250682
DOIs	https://doi.org/10.1007/978-3-031-25069-9_4
State	Published - 2023
Event	Workshops held at the 17th European Conference on Computer Vision, ECCV 2022 - Tel Aviv, Israel Duration: 23 Oct 2022 → 27 Oct 2022

Publication series

Name	Lecture Notes in Computer Science
Volume	13804 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Workshops held at the 17th European Conference on Computer Vision, ECCV 2022
Country/Territory	Israel
City	Tel Aviv
Period	23/10/22 → 27/10/22

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Anomaly detection
Representation learning
Self-Supervised learning

Access to Document

10.1007/978-3-031-25069-9_4

Cite this

Reiss, T., Cohen, N., Horwitz, E., Abutbul, R., & Hoshen, Y. (2023). Anomaly Detection Requires Better Representations. In L. Karlinsky, T. Michaeli, & K. Nishino (Eds.), Computer Vision – ECCV 2022 Workshops, Proceedings (pp. 56-68). (Lecture Notes in Computer Science; Vol. 13804 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-25069-9_4

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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.; Workshops held at the 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",

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Reiss, T, Cohen, N, Horwitz, E, Abutbul, R & Hoshen, Y 2023, Anomaly Detection Requires Better Representations. in L Karlinsky, T Michaeli & K Nishino (eds), Computer Vision – ECCV 2022 Workshops, Proceedings. Lecture Notes in Computer Science, vol. 13804 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 56-68, Workshops held at the 17th European Conference on Computer Vision, ECCV 2022, Tel Aviv, Israel, 23/10/22. https://doi.org/10.1007/978-3-031-25069-9_4

Anomaly Detection Requires Better Representations. / Reiss, Tal; Cohen, Niv; Horwitz, Eliahu et al.
Computer Vision – ECCV 2022 Workshops, Proceedings. ed. / Leonid Karlinsky; Tomer Michaeli; Ko Nishino. Springer Science and Business Media Deutschland GmbH, 2023. p. 56-68 (Lecture Notes in Computer Science; Vol. 13804 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Anomaly Detection Requires Better Representations

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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