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Semi-supervised anomaly detection methods leverage a few anomaly examples toyield 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 unlabeleddata are employed as inliers for model training; 2) only discrete supervisioninformation (such as binary or ordinal data labels) is exploited, which leadsto suboptimal learning of anomaly scores that essentially take on a continuousdistribution. Therefore, this paper proposes a novel semi-supervised anomalydetection method, which devises \textit{contamination-resilient continuoussupervisory signals}. Specifically, we propose a mass interpolation method todiffuse the abnormality of labeled anomalies, thereby creating new data sampleslabeled with continuous abnormal degrees. Meanwhile, the contaminated area canbe covered by new data samples generated via combinations of data with correctlabels. A feature learning-based objective is added to serve as an optimizationconstraint to regularize the network and further enhance the robustness w.r.t.anomaly contamination. Extensive experiments on 11 real-world datasets showthat our approach significantly outperforms state-of-the-art competitors by20%-30% in AUC-PR and obtains more robust and superior performance in settingswith different anomaly contamination levels and varying numbers of labeledanomalies. The source code is available at https://github.com/xuhongzuo/rosas/.

RoSAS: Deep Semi-Supervised Anomaly Detection with Contamination-Resilient Continuous Supervision