Semi-Supervised Anomaly Detection Using Restricted Distribution Transformation.

Anomaly detection (AD) is typically regarded as an unsupervised learning task, where the training data either do not contain any anomalous samples or contain only a few unlabeled anomalous samples. In fact, in many real scenarios such as fault diagnosis and disease detection, a small number of anomalous samples labeled by domain experts are often available during the training phase, which makes semi-supervised AD (SAD) more appealing, though the related study is quite limited. Existing semi-supervised AD methods directly add optimization terms of anomalous samples to the optimization objective of unsupervised AD (UAD), where the effects of the limited labeled anomalous data on the optimization process become trivial and they cannot fully contribute to the detection task. To cover the shortage, in this work, we propose a novel semi-supervised AD method to fully use the limited labeled anomalous data and further to boost detection performance. The proposed method learns a nonlinear transformation to project normal data into a compact target distribution and simultaneously to project exposed anomalous samples into another target distribution, where the two target distributions do not overlap each other. The goal is difficult to achieve because of the scarcity of anomalous samples. To address this problem, we propose to generate a large number of intermediate samples interpolating between normal and anomalous data and project them into a third target distribution lying between the aforementioned two target distributions. Empirical results on multiple benchmarks with varying domains demonstrate the superiority of our method over existing supervised and semi-supervised AD methods.