记忆组成无法告诉我们的异常检测相关内容
What Memory Composition Does Not Tell Us About Anomaly Detection
- RatelSoft
- Guangdong Provincial Laboratory of Traditional Chinese Medicine（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总结：
该研究针对基于记忆的异常检测器，对比不同覆盖选择器，提出CLEANCON方法可降低记忆污染并提升P-AP，还发现记忆污染程度与P-AP排序无对应关系。
AI中文摘要：
基于记忆的异常检测器会存储正常训练图像块，并将测试图像块与该记忆进行打分。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来探究这种耦合关系。在固定表示和记忆预算下，我们比较随机、medoid（中心点）、局部和全局覆盖选择器。随后使用CLEANCON（一种袋外交叉图像支持门，用于在固定表示、绝对记忆大小、构建器和推理规则的同时改变候选图像的资格）。全局覆盖会严重过度代表稀疏污染。CLEANCON将最终记忆污染降至约零，并在所有12次匹配比较中提高了类别平均精度（P-AP）。然而，在保留范围内，污染最低的记忆并未达到最高P-AP；随着污染上升，性能仍在持续提升。因此，记忆污染无法按P-AP对所得记忆进行排序。
英文摘要：
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
