Anomaly Detection Based on Unsupervised Disentangled Representation Learning in Combination with Manifold Learning

Xiaoyan Li, Iluju Kiringa, Tet Yeap, Xiaodan Zhu, Yifeng Li

Keywords: anomaly detection, disentangled representation learning, manifold learning

TL;DR: We developed anomaly detection framework based on beta-VAE and t-SNE

Abstract: Identifying anomalous samples from highly complex and unstructured data is a crucial but challenging task in a variety of intelligent systems. In this paper, we present a novel deep anomaly detection framework named AnoDM (standing for Anomaly detection based on unsupervised Disentangled representation learning and Manifold learning). The disentanglement learning is currently implemented by beta-VAE for automatically discovering interpretable factorized latent representations in a completely unsupervised manner. The manifold learning is realized by t-SNE for projecting the latent representations to a 2D map. We define a new anomaly score function by combining beta-VAE's reconstruction error in the raw feature space and local density estimation in the t-SNE space. AnoDM was evaluated on both image and time-series data and achieved better results than models that use just one of the two measures and other deep learning methods.

Code: https://drive.google.com/file/d/1KSibzY4z87yTIHFvOr29iOCoxdxt-Jzs/view?usp=sharing