MADCluster: Model-agnostic Anomaly Detection with Self-supervised Clustering Network

Sangyong Lee, Subo Hwang

Keywords: Anomaly Detection, Model-agnostic, Self-supervised Clustering, Hypersphere Collapse, One-directed Adaptive loss

TL;DR: We propose MADCluster, a model-agnostic anomaly detection framework utilizing self-supervised clustering including a new loss function called ‘One-directed Adaptive loss’; We address the 'hypersphere collapse’ problem to improve expressiveness.

Abstract: In this paper, we propose MADCluster, a novel model-agnostic anomaly detection framework utilizing self-supervised clustering. MADCluster is applicable to various deep learning architectures and addresses the 'hypersphere collapse' problem inherent in existing deep learning-based anomaly detection methods. The core idea is to cluster normal pattern data into a `single cluster' while simultaneously learning the cluster center and mapping data close to this center. Also, to improve expressiveness and enable effective single clustering, we propose a new 'One-directed Adaptive loss'. The optimization of this loss is mathematically proven. MADCluster consists of three main components: Base Embedder capturing high-dimensional temporal dynamics, Cluster Distance Mapping, and Sequence-wise Clustering for continuous center updates. Its model-agnostic characteristics are achieved by applying various architectures to the Base Embedder. Experiments on four time series benchmark datasets demonstrate that applying MADCluster improves the overall performance of comparative models. In conclusion, the compatibility of MADCluster shows potential for enhancing model performance across various architectures.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Submission Number: 4296