Learning Memory-guided Normality for Anomaly Detection
- URL: http://arxiv.org/abs/2003.13228v1
- Date: Mon, 30 Mar 2020 05:30:09 GMT
- Title: Learning Memory-guided Normality for Anomaly Detection
- Authors: Hyunjong Park, Jongyoun Noh, Bumsub Ham
- Abstract summary: We present an unsupervised learning approach to anomaly detection that considers the diversity of normal patterns explicitly.
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.
- Score: 33.77435699029528
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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