发表机构
RatelSoft; Guangdong Provincial Laboratory of Traditional Chinese Medicine（RatelSoft; 广东省中医药实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对基于内存的异常检测器的训练图像块选择问题，对比多种覆盖选择器，提出CLEANCON方法，可降低内存污染并提升P-AP，且发现内存污染与性能排序无对应关系。
AI 中文摘要
基于内存的异常检测器会存储标称训练图像块，并针对该内存对测试图像块打分。因此，为覆盖范围选择的图像块会成为正常参考，而不会单独检查其几何稀有性是否值得信任。我们通过稀疏训练污染探究这种耦合关系。在固定表示和内存预算下，我们比较了随机、medoid（中心）、局部和全局覆盖选择器。随后，我们使用CLEANCON（一种袋外跨图像支持门，在固定表示、绝对内存大小、构建器和推理规则的同时更改候选图像的合格性）。全局覆盖会过度代表稀疏污染。CLEANCON将最终内存污染降低至近似为零，并在所有12次匹配比较中提高了类别平均精度（P-AP）。然而，在保留范围扫描中，最低污染内存并未达到最高P-AP；随着污染上升，性能仍持续提升。因此，内存污染无法按此对生成的内存排序。代码可在两个公开链接获取。
英文摘要
Normal-only industrial anomaly detectors use patches from clean training images as normal references or reconstruction targets. This assumes that clean patches are sufficient for the normal regions encountered at test time. We test that assumption directly. On MVTec AD, admitting ground-truth-normal patches from real defect images to a DINOv2 memory candidate pool raises pixel average precision (P-AP) from 73.34 to 76.95 while keeping the encoder, test-time score, and number of stored references fixed. Patches within two patch cells of the annotated defect recover 94.70% of this gain. We then ask whether useful patches of this kind can be exposed using clean training images alone. BoundarySupport inserts a procedural synthetic defect to alter surrounding context, excludes every token intersecting the nominal insertion or a detected RGB change, and learns only from pixel-preserved neighboring patches. Across three paired seeds, the same principle improves P-AP in all six memory and reconstruction settings across MVTec, VisA, and Real-IAD. Matched controls identify the altered-context feature itself as the useful normal evidence: with synthetic input or selected positions fixed, altered-context features outperform their clean-view counterparts as both reconstruction targets and memory references. On MVTec memory, the final score change is also spatially selective, with larger reductions on normal patches next to defects than on mid-distance or far-normal patches in all 15 categories. Code is publicly available at https://github.com/jw-chae/boundary_support.
