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Applied Computational Intelligence and Soft Computing
Volume 2017, Article ID 1320780, 13 pages
Review Article

Deep Learning in Visual Computing and Signal Processing

Department of Electrical and Computer Engineering, Temple University, Philadelphia, PA 19121, USA

Correspondence should be addressed to Danfeng Xie; ude.elpmet@eix.gnefnad

Received 21 October 2016; Revised 15 December 2016; Accepted 15 January 2017; Published 19 February 2017

Academic Editor: Francesco Carlo Morabito

Copyright © 2017 Danfeng Xie et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Deep learning is a subfield of machine learning, which aims to learn a hierarchy of features from input data. Nowadays, researchers have intensively investigated deep learning algorithms for solving challenging problems in many areas such as image classification, speech recognition, signal processing, and natural language processing. In this study, we not only review typical deep learning algorithms in computer vision and signal processing but also provide detailed information on how to apply deep learning to specific areas such as road crack detection, fault diagnosis, and human activity detection. Besides, this study also discusses the challenges of designing and training deep neural networks.