在固定内存下持续保持异常检测的准确性，通过迭代贪心采样实现。
Memory-Bounded Continuation of Greedy Sampling for Continual Anomaly Detection
- 迭代贪心采样：在新任务上扩展后合并，维持核心集代表性。
- 内存恒定下仍保持高精度：在MVTecAD和VisA上11种任务调度均达顶尖性能。
- 适合在线持续异常检测场景：优于现有方法，无灾难性遗忘问题。
贪心采样可生成紧凑且具代表性的正常数据摘要，对依赖远离正常性度量的异常检测至关重要。在任务连续到达的持续异常检测中，若允许无界内存，可通过累积核心集（coreset）简单扩展。但实际部署需固定内存，核心集大小不随任务数增加而变化。我们发现，持续贪心采样——即对先前已采样集重复应用贪心选择——在严格内存限制下仍能有效保持代表性。尽管每步需丢弃数据以满足内存约束，核心集质量仅渐进下降而非崩溃，确保跨任务的可靠异常检测。理论证明表明，所得持续贪心核心集与理想核心集之间的差距有界。我们基于此提出ContCore：通过在新任务特征上贪心扩展，再贪心合并以强制内存预算。相比易受灾难性遗忘的神经方法或需无界内存的朴素累积，ContCore在固定内存下具备理论保障。实验证明，其在MVTecAD和VisA上的11种任务调度中表现最优，并成功拓展至在线持续异常检测，而此前方法显著退化。代码见：https://github.com/jungyg/ContCore
原文摘要 · Abstract (English)
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
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