Constricting Normal Latent Space for Anomaly Detection with Normal-only
  Training Data
Marcella Astrid Muhammad Zaigham Zaheer Seung-Ik Lee
In order to devise an anomaly detection model using only normal training
data, an autoencoder (AE) is typically trained to reconstruct the data. As a
result, the AE can extract normal representations in its latent space. During
test time, since AE is not trained using real anomalies, it is expected to
poorly reconstruct the anomalous data. However, several researchers have
observed that it is not the case. In this work, we propose to limit the
reconstruction capability of AE by introducing a novel latent constriction
loss, which is added to the existing reconstruction loss. By using our method,
no extra computational cost is added to the AE during test time. Evaluations
using three video anomaly detection benchmark datasets, i.e., Ped2, Avenue, and
ShanghaiTech, demonstrate the effectiveness of our method in limiting the
reconstruction capability of AE, which leads to a better anomaly detection
model.