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（SSAD）方法通过利用少量但具有指导意义的异常实例来增强无监督异常检测（UAD）的有效性。然而，同类正常数据相对于异常的绝对优势会使SSAD模型偏向于难以有效感知异常。为应对这一问题，实现高度不平衡的正常与异常数据之间的均衡监督，我们提出了一种名为AnoOnly（仅关注异常）的新框架。与依赖严格损失监督的现有SSAD方法不同，AnoOnly暂停了这种监督，并为正常数据引入一种弱监督形式。该弱监督通过批归一化实现，隐式地对正常数据进行聚类学习。当集成到现有SSAD方法中时，所提出的AnoOnly在多种模型和数据集上展现出显著性能提升，达到了新的最优性能。此外，当面临数据污染时，我们的AnoOnly对标签噪声具有天然鲁棒性。我们的代码已在https://github.com/cool-xuan/AnoOnly公开。