Journal ArticleDOI
A Survey on Deep Learning: Algorithms, Techniques, and Applications
Samira Pouyanfar,Saad Sadiq,Yilin Yan,Haiman Tian,Yudong Tao,Maria Presa Reyes,Mei-Ling Shyu,Shu-Ching Chen,S. Sitharama Iyengar +8 more
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TLDR
A comprehensive review of historical and recent state-of-the-art approaches in visual, audio, and text processing; social network analysis; and natural language processing is presented, followed by the in-depth analysis on pivoting and groundbreaking advances in deep learning applications.Abstract:
The field of machine learning is witnessing its golden era as deep learning slowly becomes the leader in this domain. Deep learning uses multiple layers to represent the abstractions of data to build computational models. Some key enabler deep learning algorithms such as generative adversarial networks, convolutional neural networks, and model transfers have completely changed our perception of information processing. However, there exists an aperture of understanding behind this tremendously fast-paced domain, because it was never previously represented from a multiscope perspective. The lack of core understanding renders these powerful methods as black-box machines that inhibit development at a fundamental level. Moreover, deep learning has repeatedly been perceived as a silver bullet to all stumbling blocks in machine learning, which is far from the truth. This article presents a comprehensive review of historical and recent state-of-the-art approaches in visual, audio, and text processing; social network analysis; and natural language processing, followed by the in-depth analysis on pivoting and groundbreaking advances in deep learning applications. It was also undertaken to review the issues faced in deep learning such as unsupervised learning, black-box models, and online learning and to illustrate how these challenges can be transformed into prolific future research avenues.read more
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Journal ArticleDOI
Deep Learning for Generic Object Detection: A Survey
Li Liu,Li Liu,Wanli Ouyang,Xiaogang Wang,Paul Fieguth,Jie Chen,Xinwang Liu,Matti Pietikäinen +7 more
TL;DR: A comprehensive survey of the recent achievements in this field brought about by deep learning techniques, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics.
Journal ArticleDOI
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Laith Alzubaidi,Jinglan Zhang,Amjad J. Humaidi,Ayad Q. Al-Dujaili,Ye Duan,Omran Al-Shamma,José Santamaría,Mohammed A. Fadhel,Muthana Al-Amidie,Laith Farhan +9 more
TL;DR: In this paper, a comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field is provided, and the challenges and suggested solutions to help researchers understand the existing research gaps.
Journal ArticleDOI
Deep Learning in Mobile and Wireless Networking: A Survey
TL;DR: This paper bridges the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas, and provides an encyclopedic review of mobile and Wireless networking research based on deep learning, which is categorize by different domains.
Journal ArticleDOI
Review of Deep Learning Algorithms and Architectures
Ajay Shrestha,Ausif Mahmood +1 more
TL;DR: This paper reviews several optimization methods to improve the accuracy of the training and to reduce training time, and delve into the math behind training algorithms used in recent deep networks.
Journal ArticleDOI
A review of vibration-based damage detection in civil structures : from traditional methods to Machine Learning and Deep Learning applications
TL;DR: This paper aims to fulfill the gap by presenting the highlights of the traditional methods and provide a comprehensive review of the most recent applications of ML and DL algorithms utilized for vibration-based structural damage detection in civil structures.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
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ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
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Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.