Papers › CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localization

CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localization

9 Jun 2022arXiv:2206.04325archive 2025-07-28

Sungwook Lee, SeungHyun Lee, Byung Cheol Song

For a long time, anomaly localization has been widely used in industries. Previous studies focused on approximating the distribution of normal features without adaptation to a target dataset. However, since anomaly localization should precisely discriminate normal and abnormal features, the absence of adaptation may make the normality of abnormal features overestimated. Thus, we propose Coupled-hypersphere-based Feature Adaptation (CFA) which accomplishes sophisticated anomaly localization using features adapted to the target dataset. CFA consists of (1) a learnable patch descriptor that learns and embeds target-oriented features and (2) scalable memory bank independent of the size of the target dataset. And, CFA adopts transfer learning to increase the normal feature density so that abnormal features can be clearly distinguished by applying patch descriptor and memory bank to a pre-trained CNN. The proposed method outperforms the previous methods quantitatively and qualitatively. For example, it provides an AUROC score of 99.5% in anomaly detection and 98.5% in anomaly localization of MVTec AD benchmark. In addition, this paper points out the negative effects of biased features of pre-trained CNNs and emphasizes the importance of the adaptation to the target dataset. The code is publicly available at https://github.com/sungwool/CFA_for_anomaly_localization.

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Code

sungwool/cfa_for_anomaly_localization officialmentioned in papermentioned on GitHubpytorch report

openvinotoolkit/anomalib pytorchApache-2.0 report

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Tasks

Anomaly Detection Anomaly Localization Transfer Learning Unsupervised Anomaly Detection

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Anomaly Detection	MVTec AD	CFA	Detection AUROC	99.3	#40 of 148	Archive leaderboard	report
Anomaly Detection	MVTec AD	CFA	Segmentation AUROC	98.2	#40 of 148	Archive leaderboard	report
Anomaly Detection	VisA	CFA	Detection AUROC	92.0	#27 of 50	Archive leaderboard	report
Anomaly Detection	VisA	CFA	Segmentation AUPRO (until 30% FPR)	55.1	#27 of 50	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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