Papers › Collaborative Discrepancy Optimization for Reliable Image Anomaly Localization

Collaborative Discrepancy Optimization for Reliable Image Anomaly Localization

17 Feb 2023IEEE Transactions on Industrial Informatics 2023 2arXiv:2302.08769archive 2025-07-28

Yunkang Cao, Xiaohao Xu, Zhaoge Liu, Weiming Shen

Most unsupervised image anomaly localization methods suffer from overgeneralization because of the high generalization abilities of convolutional neural networks, leading to unreliable predictions. To mitigate the overgeneralization, this study proposes to collaboratively optimize normal and abnormal feature distributions with the assistance of synthetic anomalies, namely collaborative discrepancy optimization (CDO). CDO introduces a margin optimization module and an overlap optimization module to optimize the two key factors determining the localization performance, i.e., the margin and the overlap between the discrepancy distributions (DDs) of normal and abnormal samples. With CDO, a large margin and a small overlap between normal and abnormal DDs are obtained, and the prediction reliability is boosted. Experiments on MVTec2D and MVTec3D show that CDO effectively mitigates the overgeneralization and achieves great anomaly localization performance with real-time computation efficiency. A real-world automotive plastic parts inspection application further demonstrates the capability of the proposed CDO. Code is available on https://github.com/caoyunkang/CDO.

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conv1x1 caoyunkang/CDO/model/cdo.py official repository ran · our draft was wrong MIT (permissive) · d9def42110729a85 · report
conv3x3 caoyunkang/CDO/model/cdo.py official repository ran · our draft was wrong MIT (permissive) · 160bb14bd76201b4 · report
gaussian caoyunkang/CDO/loss/base_loss.py official repository ran · honoured contract fingerprinted MIT (permissive) · c56b7ef16f309a45 · report
compute_lcs_in_chunks caoyunkang/CDO/loss/ikd_loss.py official repository unverified MIT (permissive) · 99d9f4c3d26484ea · report
create_window caoyunkang/CDO/loss/base_loss.py official repository unverified MIT (permissive) · 443341084ce76798 · report
ssim caoyunkang/CDO/loss/base_loss.py official repository unverified MIT (permissive) · bff794bd9bd73f94 · report

Tasks

Anomaly DetectionAnomaly Localization

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Anomaly Detection MVTEC 3D-AD CDO Segmentation AUPRO 93.75 #1 of 2 Archive leaderboard report
Anomaly Detection MVTec AD CDO FPS 79.6 (exclude data inputting time), 18.7 (contain data inputting time) #124 of 148 Archive leaderboard report
Anomaly Detection MVTec AD CDO Segmentation AUPRO 96.50 #124 of 148 Archive leaderboard report
Anomaly Detection MVTec AD CDO Segmentation AUROC 98.70 #124 of 148 Archive leaderboard report

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