What Remains Normal? Clean Images Miss Useful Near-Defect Normal Patches for Anomaly Detection

2026-08-24

AI Summary

研究解决了记忆式异常检测器中稀疏污染问题，通过使用CLEANCON方法筛选正常补丁，减少内存污染并提高检测性能。

Abstract

Memory-based anomaly detectors store nominal training patches and score test patches against this memory. A patch selected for coverage therefore becomes a nor- mal reference without a separate check that geometric rarity makes it safe to trust. We probe this coupling with sparse training contamination. Under fixed representa- tions and memory budgets, we compare random, medoid, local, and global coverage selectors. We then use CLEANCON, an out-of-bag cross-image support gate that changes candidate-image eligibility while fixing the representation, absolute mem- ory size, builder, and inference rule. Global coverage strongly over-represents sparse contamination. CLEANCON reduces final-memory contamination to approx- imately zero and increases category-macro P-AP in all 12 matched comparisons. Yet along a retention sweep, the lowest-contamination memory does not attain the highest P-AP; performance continues to improve while contamination rises. Mem- ory contamination therefore does not order the resulting memories by P-AP.Code is publicly available at https://github.com/jw-chae/cleancon.

Problem

• anomaly detection
• memory-based
• sparse contamination
• normal patches
• CLEANCON

Innovation

• CLEANCON
• memory contamination
• anomaly detection
• cross-image support gate
• P-AP