Learning Memory-guided Normality for Anomaly Detection

TL;DR

This paper introduces an unsupervised anomaly detection method that uses a memory module to explicitly model diverse normal patterns, improving detection accuracy over traditional CNN-based reconstruction methods.

Contribution

It proposes a novel memory-augmented framework with a new update scheme and feature losses to better capture normal data diversity and reduce abnormal reconstruction.

Findings

Outperforms state-of-the-art methods on standard benchmarks

Effectively models diverse normal patterns with a memory module

Reduces false positives by lessening CNNs' reconstruction of anomalies

Abstract

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.