Deep Learning in Visual Computing and Signal Processing

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 processin...

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Vydáno v:Applied Computational Intelligence and Soft Computing Ročník 2017; číslo 2017; s. 4 - 16
Hlavní autoři: Xie, Danfeng, Bai, Li, Zhang, Lei
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cairo, Egypt Hindawi Limiteds 01.01.2017
Hindawi Publishing Corporation
Hindawi
John Wiley & Sons, Inc
Wiley
Témata:
Algorithms
Artificial intelligence
Brain
Computation
Computational linguistics
Cracks
Data mining
Fault diagnosis
Intelligence
Language processing
Machine learning
Machine vision
Moving object recognition
Natural language interfaces
Neural networks
Researchers
Signal processing
Studies
Theory
ISSN:1687-9724, 1687-9732
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Popis
Shrnutí: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.
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ISSN:1687-9724
1687-9732
DOI:10.1155/2017/1320780