RoSAS: Deep Semi-Supervised Anomaly Detection with
  Contamination-Resilient Continuous Supervision
Hongzuo Xu Yijie Wang Guansong Pang Songlei Jian Ning Liu Yongjun Wang
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/.