用少量异常样本训练模型，实现对未知异常的精准检测。
A Multi-directional Meta-Learning Framework for Class-Generalizable Anomaly Detection
- 内层学正常数据分布，外层用少量异常样本来校准分类边界。
- 在多个任务轮次中迭代优化，显著提升对未见异常的泛化能力。
- 适合数据稀缺场景，尤其适用于异常样本难获取的工业检测。
本文针对类别可泛化的异常检测问题，旨在仅利用正常数据和少量异常数据构建统一模型，以检测完全未见过的异常（即分布外，OOD）类别。由于异常数据稀少且标注成本高，我们提出一种多方向元学习算法：内层优化聚焦于学习正常数据的流形结构（表示学习）；外层则通过少量异常样本进行元调优，最大化正常与异常样本间的Softmax置信度差距（决策面校准），将正常样本视为分布内（ID），异常样本视为分布外（OOD）。通过在多个主要包含正常样本、少量异常样本的任务轮次中反复迭代该过程，实现了多方向元学习框架。这种双层优化机制结合多方向训练，显著增强了模型对未见异常类别的泛化能力。
原文摘要 · Abstract (English)
In this paper, we address the problem of class-generalizable anomaly detection, where the objective is to develop a unified model by focusing our learning on the available normal data and a small amount of anomaly data in order to detect the completely unseen anomalies, also referred to as the out-of-distribution (OOD) classes. Adding to this challenge is the fact that the anomaly data is rare and costly to label. To achieve this, we propose a multidirectional meta-learning algorithm -- at the inner level, the model aims to learn the manifold of the normal data (representation); at the outer level, the model is meta-tuned with a few anomaly samples to maximize the softmax confidence margin between the normal and anomaly samples (decision surface calibration), treating normals as in-distribution (ID) and anomalies as out-of-distribution (OOD). By iteratively repeating this process over multiple episodes of predominantly normal and a small number of anomaly samples, we realize a multidirectional meta-learning framework. This two-level optimization, enhanced by multidirectional training, enables stronger generalization to unseen anomaly classes.
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