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.

通过引入一种新的潜变量约束损失来限制自动编码器的重构能力，本研究在不增加额外计算成本的情况下，提出了一种有效的限制异常重构能力的自动编码器的方法，从而提高异常检测模型的性能。