面向持续异常检测的内存受限贪心采样的延续方法
Memory-Bounded Continuation of Greedy Sampling for Continual Anomaly Detection
浏览论文内容
中文总结 AI 辅助
该研究针对持续异常检测提出ContCore方法，通过延续贪心采样构建固定内存的核心集，在MVTecAD、VisA数据集的11种任务调度及在线设置下实现最优性能。
中文摘要 AI 辅助
贪心采样可生成紧凑且具有代表性的正常数据摘要，这对依赖于与正常数据距离测量的可靠异常检测至关重要。对于任务按顺序到达的持续异常检测，通过核心集积累扩展贪心采样在无界内存下是直接可行的。但实际部署需要固定内存，即核心集大小不随任务数量变化。我们观察到，对先前贪心采样集迭代应用贪心选择的延续贪心采样，能在严格内存限制下有效保持代表性。尽管每一步为满足内存约束会丢弃数据，但核心集质量会平缓下降而非灾难性下降，从而能在所有任务中实现可靠的异常检测。我们提供理论证明，表明所得的贪心延续核心集与理想核心集的近似误差有界。我们将该原理实例化为ContCore，其通过对新任务特征进行贪心扩展，再经贪心整合以强制执行内存预算来构建贪心延续核心集。与易受灾难性遗忘影响的神经方法或需要无界内存的朴素核心集积累不同，ContCore在具有理论保证的同时维持固定内存。实验表明，ContCore在MVTecAD和VisA的11种任务调度中实现了最先进的性能，且能有效扩展到在线持续异常检测设置，而现有方法在该设置下会显著退化。代码：this https URL
英文摘要
Greedy sampling produces a compact yet representative summary of normal data, which is essential for reliable anomaly detection that relies on measuring distance from normality. For continual anomaly detection where tasks arrive sequentially, extending greedy sampling is straightforward with unbounded memory through coreset accumulation. However, practical deployment requires fixed memory where the coreset size remains constant regardless of task count. We observe that continued greedy sampling, which iteratively applies greedy selection over previously greedy-sampled sets, effectively preserves representativeness under strict memory limits. Despite discarding data at each step to satisfy the memory constraint, coreset quality degrades gracefully rather than catastrophically, enabling reliable anomaly detection across the tasks. We provide theoretical justification by showing that resulting greedy-continued coreset approximates the oracle coreset within a bounded gap. We instantiate this principle in ContCore, which constructs a greedy-continued coreset through greedy expansion on new task features followed by greedy consolidation to enforce the memory budget. Unlike neural methods susceptible to catastrophic forgetting or naive coreset accumulation requiring unbounded memory, ContCore maintains fixed memory with theoretical guarantees. Empirically, ContCore achieves state-of-the-art performance across 11 task schedules on MVTecAD and VisA, and extends effectively to online continual AD settings where prior methods degrade significantly. Code: https://github.com/jungyg/ContCore
发表机构
- Northeastern University（东北大学）
- AIVEX Inc.（AIVEX公司）
- Mitsubishi Electric Research Laboratories (MERL)（三菱电机研究实验室）
机构由 AI 辅助整理，请以论文原文为准。
