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Recent advances in convolutional neural networks - 2018

Research Area:  Machine Learning

Abstract:

In the last few years, deep learning has led to very good performance on a variety of problems, such as visual recognition, speech recognition and natural language processing. Among different types of deep neural networks, convolutional neural networks have been most extensively studied. Leveraging on the rapid growth in the amount of the annotated data and the great improvements in the strengths of graphics processor units, the research on convolutional neural networks has been emerged swiftly and achieved state-of-the-art results on various tasks. In this paper, we provide a broad survey of the recent advances in convolutional neural networks. We detailize the improvements of CNN on different aspects, including layer design, activation function, loss function, regularization, optimization and fast computation. Besides, we also introduce various applications of convolutional neural networks in computer vision, speech and natural language processing.

Author(s) Name:  Jiuxiang Gu, Zhenhua Wang, Jason Kuen, Lianyang Ma, Amir Shahroudy, Ting Liu, Xingxing Wang, Gang Wang, Jianfei Cai, Tsuhan Chen

Journal name:  Pattern Recognition

Conferrence name:  

Publisher name:  Elsevier

DOI:  10.1016/j.patcog.2017.10.013

Volume Information:  Volume 77, May 2018, Pages 354-377