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意义的异常实例，有效提升了无监督异常检测的性能。然而，同质正常数据相较于异常数据的绝对优势，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严重失衡的正常与异常数据间的平衡监督，我们提出了一种名为AnoOnly（仅关注异常）的新型框架。不同于现有依赖严格损失监督的半监督异常检测方法，AnoOnly暂停了严格监督，并引入一种针对正常数据的弱监督形式。该弱监督通过批归一化实现，其隐式地对正常数据执行聚类学习。当集成至现有半监督异常检测方法时，所提出的AnoOnly在多种模型和数据集上展现出显著的性能提升，并取得了新的最优结果。此外，在遭遇数据污染时，我们的AnoOnly天然具备对标签噪声的鲁棒性。我们的代码已开源在 https://github.com/cool-xuan/AnoOnly。