Semi-supervised anomaly detection methods leverage a few anomaly examples to yield drastically improved performance compared to unsupervised models. However, they still suffer from two limitations: 1) unlabeled anomalies (i.e., anomaly contamination) may mislead the learning process when all the unlabeled data are employed as inliers for model training; 2) only discrete supervision information (such as binary or ordinal data labels) is exploited, which leads to suboptimal learning of anomaly scores that essentially take on a continuous distribution. Therefore, this paper proposes a novel semi-supervised anomaly detection method, which devises \textit{contamination-resilient continuous supervisory signals}. Specifically, we propose a mass interpolation method to diffuse the abnormality of labeled anomalies, thereby creating new data samples labeled with continuous abnormal degrees. Meanwhile, the contaminated area can be covered by new data samples generated via combinations of data with correct labels. A feature learning-based objective is added to serve as an optimization constraint to regularize the network and further enhance the robustness w.r.t. anomaly contamination. Extensive experiments on 11 real-world datasets show that our approach significantly outperforms state-of-the-art competitors by 20%-30% in AUC-PR and obtains more robust and superior performance in settings with different anomaly contamination levels and varying numbers of labeled anomalies. The source code is available at https://github.com/xuhongzuo/rosas/.

翻译：半监督异常检测方法通过利用少量异常样本，相比无监督模型显著提升了性能。然而，这些方法仍存在两个局限：1）当所有未标注数据均被作为正常样本用于模型训练时，未标注的异常样本（即异常污染）可能误导学习过程；2）仅利用离散监督信息（如二值或序数数据标签），导致本质上具有连续分布的异常分数学习效果欠佳。为此，本文提出一种新型半监督异常检测方法，设计了《抗污染连续监督信号》。具体而言，我们提出一种质量插值方法，通过扩散标注异常的异常程度，生成标注连续异常程度的新数据样本。同时，利用正确标签数据组合生成的新样本可覆盖受污染区域。我们引入基于特征学习的目标函数作为优化约束以规范网络，并进一步增强对异常污染的鲁棒性。在11个真实数据集上的实验表明，本方法在AUC-PR指标上以20%-30%的幅度显著优于现有最优方法，并在不同异常污染程度和不同数量标注异常场景下展现出更强鲁棒性和优越性能。源代码见https://github.com/xuhongzuo/rosas/。