  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'.
