We address the problem of anomaly detection, that is, detecting anomalous
events in a video sequence. Anomaly detection methods based on convolutional
neural networks (CNNs) typically leverage proxy tasks, such as reconstructing
input video frames, to learn models describing normality without seeing
anomalous samples at training time, and quantify the extent of abnormalities
using the reconstruction error at test time. The main drawbacks of these
approaches are that they do not consider the diversity of normal patterns
explicitly, and the powerful representation capacity of CNNs allows to
reconstruct abnormal video frames. To address this problem, we present an
unsupervised learning approach to anomaly detection that considers the
diversity of normal patterns explicitly, while lessening the representation
capacity of CNNs. To this end, we propose to use a memory module with a new
update scheme where items in the memory record prototypical patterns of normal
data. We also present novel feature compactness and separateness losses to
train the memory, boosting the discriminative power of both memory items and
deeply learned features from normal data. Experimental results on standard
benchmarks demonstrate the effectiveness and efficiency of our approach, which
outperforms the state of the art.

本文提出了一种基于无监督学习的异常检测方法，其中使用内存模块记录正常数据的原型模式，并提出了新的特征紧凑性和分离性损失来训练内存以提高区分能力和识别准确性。该方法在标准基准测试中表现良好，优于现有技术。