少样本异常检测中，利用少量异常样本提升模型性能。
Anomalous Samples for Few-Shot Anomaly Detection
- 融合零样本与记忆机制，构建多评分异常检测框架。
- 实验证明异常样本在少样本场景下显著优于正常样本。
- 提出基于增强的验证方法，优化多评分融合效果。
许多异常检测与分类方法依赖大量正常样本，假设异常数据难以获取。但在少样本场景下，仅一个标注样本就可能带来显著影响。本文研究如何利用异常样本训练二元异常分类模型。提出一种新方法，通过结合零样本与基于记忆的技术，构建多评分异常检测框架。比较异常样本与常规样本的效用，并分析其优劣。此外，提出一种基于增强的验证技术，用于优化不同异常评分的聚合，在多个工业级异常检测数据集上验证了其有效性。
原文摘要 · Abstract (English)
Several anomaly detection and classification methods rely on large amounts of non-anomalous or "normal" samples under the assump- tion that anomalous data is typically harder to acquire. This hypothesis becomes questionable in Few-Shot settings, where as little as one anno- tated sample can make a significant difference. In this paper, we tackle the question of utilizing anomalous samples in training a model for bi- nary anomaly classification. We propose a methodology that incorporates anomalous samples in a multi-score anomaly detection score leveraging recent Zero-Shot and memory-based techniques. We compare the utility of anomalous samples to that of regular samples and study the benefits and limitations of each. In addition, we propose an augmentation-based validation technique to optimize the aggregation of the different anomaly scores and demonstrate its effectiveness on popular industrial anomaly detection datasets.
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