Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning

TL;DR

This paper introduces a novel unsupervised anomaly detection method for tabular data that leverages a new supervisory signal called scale, enabling the model to learn data regularities and identify anomalies effectively.

Contribution

The paper proposes a scale learning-based anomaly detection approach that uses data-driven scale labels to improve anomaly detection performance over existing methods.

Findings

Significant improvement over state-of-the-art methods

Effective modeling of data regularities and patterns

Robust detection of anomalies in tabular data

Abstract

Due to the unsupervised nature of anomaly detection, the key to fueling deep models is finding supervisory signals. Different from current reconstruction-guided generative models and transformation-based contrastive models, we devise novel data-driven supervision for tabular data by introducing a characteristic -- scale -- as data labels. By representing varied sub-vectors of data instances, we define scale as the relationship between the dimensionality of original sub-vectors and that of representations. Scales serve as labels attached to transformed representations, thus offering ample labeled data for neural network training. This paper further proposes a scale learning-based anomaly detection method. Supervised by the learning objective of scale distribution alignment, our approach learns the ranking of representations converted from varied subspaces of each data instance. Through this proxy task, our approach models inherent regularities and patterns within data, which well describes data "normality". Abnormal degrees of testing instances are obtained by measuring whether they fit these learned patterns. Extensive experiments show that our approach leads to significant improvement over state-of-the-art generative/contrastive anomaly detection methods.