Open AccessPosted Content
Deep Learning for Anomaly Detection: A Survey.
TLDR
A structured and comprehensive overview of research methods in deep learning-based anomaly detection, grouped state-of-the-art research techniques into different categories based on the underlying assumptions and approach adopted.Abstract:
Anomaly detection is an important problem that has been well-studied within diverse research areas and application domains. The aim of this survey is two-fold, firstly we present a structured and comprehensive overview of research methods in deep learning-based anomaly detection. Furthermore, we review the adoption of these methods for anomaly across various application domains and assess their effectiveness. We have grouped state-of-the-art research techniques into different categories based on the underlying assumptions and approach adopted. Within each category we outline the basic anomaly detection technique, along with its variants and present key assumptions, to differentiate between normal and anomalous behavior. For each category, we present we also present the advantages and limitations and discuss the computational complexity of the techniques in real application domains. Finally, we outline open issues in research and challenges faced while adopting these techniques.read more
Citations
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Journal ArticleDOI
Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images
TL;DR: Li et al. as discussed by the authors proposed a COVID-19 Lung Infection Segmentation Deep Network ( Inf-Net) to automatically identify infected regions from chest CT slices, where a parallel partial decoder is used to aggregate the high-level features and generate a global map.
Journal ArticleDOI
Deep Learning for Anomaly Detection: A Review
TL;DR: This article surveys the research of deep anomaly detection with a comprehensive taxonomy, covering advancements in 3 high-level categories and 11 fine-grained categories of the methods and discusses how they address the aforementioned challenges.
Posted ContentDOI
Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Scans
TL;DR: A novel COVID-19 Lung Infection Segmentation Deep Network (Inf-Net) is proposed to automatically identify infected regions from chest CT scans and outperforms most cutting-edge segmentation models and advances the state-of-the-art technology.
Posted Content
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder
Yong Shean Chong,Yong Haur Tay +1 more
TL;DR: In this article, a spatiotemporal architecture for anomaly detection in videos including crowded scenes is proposed, which includes two main components, one for spatial feature representation, and one for learning the temporal evolution of the spatial features.
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.
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