Normality-Calibrated Autoencoder for Unsupervised Anomaly Detection on Data Contamination

Jongmin Yu, Hyeontaek Oh, Minkyung Kim, Junsik Kim

Keywords: Generative model, autoencoder, adversarial learning, anomaly detection

TL;DR: 5pages, including github url for source code

Abstract: In this paper, we propose Normality-Calibrated Autoencoder (NCAE), which can boost anomaly detection performance on the contaminated datasets without any prior information or explicit abnormal samples in the training phase. The NCAE adversarially generates high confident normal samples from a latent space having low entropy and leverages them to predict abnormal samples in a training dataset. NCAE is trained to minimise reconstruction errors in uncontaminated samples and maximise reconstruction errors in contaminated samples. The experimental results demonstrate that our method outperforms shallow, hybrid, and deep methods for unsupervised anomaly detection and achieves comparable performance compared with semi-supervised methods using labelled anomaly samples in the training phase. The source code is publicly available on 'https://github.com/andreYoo/NCAE_UAD.git'.

Community Implementations: [ 3 code implementations](https://www.catalyzex.com/paper/arxiv:2110.14825/code)