Deep evolving semi-supervised anomaly detection

TL;DR

This paper formalizes continual semi-supervised anomaly detection, introduces a baseline variational autoencoder model with deep generative replay, and demonstrates promising results in handling streaming data with labeled and unlabeled anomalies.

Contribution

It proposes a formal problem setup for CSAD, introduces a baseline deep generative model, and evaluates outlier rejection methods in a continual learning context.

Findings

EVT-based anomaly detection shows promising results.

Outlier rejection often surpasses EWC baseline.

The model adapts to varying labeled/unlabeled data in streams.

Abstract

The aim of this paper is to formalise the task of continual semi-supervised anomaly detection (CSAD), with the aim of highlighting the importance of such a problem formulation which assumes as close to real-world conditions as possible. After an overview of the relevant definitions of continual semi-supervised learning, its components, anomaly detection extension, and the training protocols; the paper introduces a baseline model of a variational autoencoder (VAE) to work with semi-supervised data along with a continual learning method of deep generative replay with outlier rejection. The results show that such a use of extreme value theory (EVT) applied to anomaly detection can provide promising results even in comparison to an upper baseline of joint training. The results explore the effects of how much labelled and unlabelled data is present, of which class, and where it is located in the data stream. Outlier rejection shows promising initial results where it often surpasses a baseline method of Elastic Weight Consolidation (EWC). A baseline for CSAD is put forward along with the specific dataset setups used for reproducability and testability for other practitioners. Future research directions include other CSAD settings and further research into efficient continual hyperparameter tuning.