Semi-supervised anomaly detection (SSAD) methods have demonstrated their effectiveness in enhancing unsupervised anomaly detection (UAD) by leveraging few-shot but instructive abnormal instances. However, the dominance of homogeneous normal data over anomalies biases the SSAD models against effectively perceiving anomalies. To address this issue and achieve balanced supervision between heavily imbalanced normal and abnormal data, we develop a novel framework called AnoOnly (Anomaly Only). Unlike existing SSAD methods that resort to strict loss supervision, AnoOnly suspends it and introduces a form of weak supervision for normal data. This weak supervision is instantiated through the utilization of batch normalization, which implicitly performs cluster learning on normal data. When integrated into existing SSAD methods, the proposed AnoOnly demonstrates remarkable performance enhancements across various models and datasets, achieving new state-of-the-art performance. Additionally, our AnoOnly is natively robust to label noise when suffering from data contamination. Our code is publicly available at https://github.com/cool-xuan/AnoOnly.

翻译：摘要：半监督异常检测方法通过利用少量但具有指导性的异常样本，显著提升了无监督异常检测的性能。然而，同质化的正常数据相较于异常样本在数量上的绝对优势，导致半监督异常检测模型难以有效感知异常。为解决该问题并实现高度不平衡的正常与异常数据间的均衡监督，我们提出了一种名为AnoOnly（仅异常）的新型框架。与现有采用严格损失监督策略的半监督异常检测方法不同，AnoOnly暂停此类监督机制，并引入针对正常数据的弱监督形式。该弱监督通过批归一化技术实现，隐式地对正常数据进行聚类学习。在现有半监督异常检测方法中集成所提出的AnoOnly后，该方法在多种模型和数据集上均展现出显著性能提升，并创造了新的最佳性能记录。此外，当数据遭受污染时，我们的AnoOnly天然具有对标签噪声的鲁棒性。相关代码已开源于 https://github.com/cool-xuan/AnoOnly。