An Overview of Deep Learning Based Methods for Unsupervised and Semi-Supervised Anomaly Detection in Videos
TLDR
In this paper, the state-of-the-art deep learning based methods for video anomaly detection and categorizes them based on the type of model and criteria of detection, and provide the criteria of evaluation for spatio-temporal anomaly detection.Abstract:
Videos represent the primary source of information for surveillance applications. Video material is often available in large quantities but in most cases it contains little or no annotation for supervised learning. This article reviews the state-of-the-art deep learning based methods for video anomaly detection and categorizes them based on the type of model and criteria of detection. We also perform simple studies to understand the different approaches and provide the criteria of evaluation for spatio-temporal anomaly detection.read more
Citations
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Book ChapterDOI
GANomaly : semi-supervised anomaly detection via adversarial training.
TL;DR: In this paper, a conditional generative adversarial network (GAN) is used for anomaly detection in a one-class, semi-supervised learning paradigm, where an encoder-decoder-encoder sub-network is employed to map the input image to a lower dimension vector, which is then used to reconstruct the generated output image.
Journal ArticleDOI
A Unifying Review of Deep and Shallow Anomaly Detection
Lukas Ruff,Jacob R. Kauffmann,Robert A. Vandermeulen,Grégoire Montavon,Wojciech Samek,Marius Kloft,Thomas G. Dietterich,Klaus-Robert Müller +7 more
TL;DR: This review aims to identify the common underlying principles and the assumptions that are often made implicitly by various methods in deep learning, and draws connections between classic “shallow” and novel deep approaches and shows how this relation might cross-fertilize or extend both directions.
Journal ArticleDOI
A Unifying Review of Deep and Shallow Anomaly Detection
Lukas Ruff,Jacob R. Kauffmann,Robert A. Vandermeulen,Grégoire Montavon,Wojciech Samek,Marius Kloft,Thomas G. Dietterich,Klaus-Robert Müller +7 more
TL;DR: Deep learning approaches to anomaly detection (AD) have recently improved the state of the art in detection performance on complex data sets, such as large collections of images or text as mentioned in this paper, and led to the introduction of a great variety of new methods.
Posted Content
Deep Semi-Supervised Anomaly Detection
Lukas Ruff,Robert A. Vandermeulen,Nico Görnitz,Alexander Binder,Emmanuel Müller,Klaus-Robert Müller,Marius Kloft +6 more
TL;DR: This work presents Deep SAD, an end-to-end deep methodology for general semi-supervised anomaly detection, and introduces an information-theoretic framework for deep anomaly detection based on the idea that the entropy of the latent distribution for normal data should be lower than the entropy the anomalous distribution, which can serve as a theoretical interpretation for the method.
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Searching for New Physics with Deep Autoencoders
TL;DR: A potentially powerful new method of searching for new physics at the LHC, using autoencoders and unsupervised deep learning, which opens up the exciting possibility of training directly on actual data to discover new physics with no prior expectations or theory prejudice.
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