Papers › Anomaly Detection Requires Better Representations

Anomaly Detection Requires Better Representations

19 Oct 2022arXiv:2210.10773archive 2025-07-28

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

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.

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Code

eliahuhorwitz/3D-ADS pytorchMIT report

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Tasks

3D Anomaly Detection and Segmentation Anomaly Detection Representation Learning Self-Supervised Learning

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Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Anomaly Detection	ODDS	kNN	AUROC	0.902	#1 of 3	Archive leaderboard	report
Anomaly Detection	ODDS	kNN	F1	0.699	#1 of 3	Archive leaderboard	report
Anomaly Detection	ODDS	ICL	AUROC	0.889	#2 of 3	Archive leaderboard	report
Anomaly Detection	ODDS	ICL	F1	0.681	#2 of 3	Archive leaderboard	report
Anomaly Detection	ODDS	GOAD	AUROC	0.782	#3 of 3	Archive leaderboard	report
Anomaly Detection	ODDS	GOAD	F1	0.544	#3 of 3	Archive leaderboard	report
Anomaly Detection	One-class CIFAR-10	DINO-FT	AUROC	98.4	#8 of 36	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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