Latent feature reconstruction for unsupervised anomaly detection

Anomalies (or outliers) indicate a minority of data items that are quite different from the majority (inliers) of a dataset in a certain aspect. Unsupervised anomaly detection (UAD) is an important but not yet extensively studied research topic. Recent deep learning based methods exploit the reconstruction gap between inliers and outliers to discriminate them. However, it is observed that the reconstruction gap often decreases rapidly as the training process goes. And there is no reasonable way to set the training stop point. To support effective UAD, we propose a new UAD framework by introducing a Latent Feature Reconstruction (LFR) layer that can be applied to recent UAD methods. The LFR layer acts as a regularizer to constrain the latent features in a low-rank subspace from which inliers can be reconstructed well while outliers cannot. We develop two new UAD methods by implementing the proposed framework with autoencoder architecture and geometric transformation scheme. Experiments on five benchmarks show that our proposed methods can achieve state-of-the-art performance in most cases.