Recent advances in convolutional neural networks
Jiuxiang Gu,Zhenhua Wang,Jason Kuen,Lianyang Ma,Amir Shahroudy,Bing Shuai,Ting Liu,Xingxing Wang,Gang Wang,Jianfei Cai,Tsuhan Chen +10 more
TLDR
A broad survey of the recent advances in convolutional neural networks can be found in this article, where the authors discuss the improvements of CNN on different aspects, namely, layer design, activation function, loss function, regularization, optimization and fast computation.About:
This article is published in Pattern Recognition.The article was published on 2018-05-01 and is currently open access. It has received 3125 citations till now. The article focuses on the topics: Deep learning & Convolutional neural network.read more
Citations
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Optical wave gauging using deep neural networks
Daniel Buscombe,Roxanne J. Carini,Shawn R. Harrison,Shawn R. Harrison,C. Chris Chickadel,Jonathan A. Warrick +5 more
TL;DR: In this paper, the optical wave gauge (OWG) consists of a deep convolutional neural network (CNN) with additional layers to distill the feature information into lower dimensional spaces, and a final layer of dense neurons to predict continuously varying quantities.
Journal ArticleDOI
Prediction of material removal rate in chemical mechanical polishing via residual convolutional neural network
TL;DR: Data-driven approaches to predict MRR based on deep learning methods to pursue better prediction performance are proposed and Experimental results show that the prediction performance of ResCNN outperforms all the existing approaches reported in the literature.
Journal ArticleDOI
Depth-based subgraph convolutional auto-encoder for network representation learning
TL;DR: This paper proposes a new graph convolutional autoencoder architecture based on a depth-based representation of graph structure, referred to as the Depth-based subgraph convolved autoenCoder (DS-CAE), which integrates both the global topological and local connectivity structures within a graph.
Journal ArticleDOI
Comparing convolutional neural networks and preprocessing techniques for HEp-2 cell classification in immunofluorescence images.
TL;DR: It is suggested that most CNNs perform better on non-preprocessed images when trained from scratch on the analyzed dataset, and that data augmentation can improve the results from all models.
Journal ArticleDOI
Handwritten Hindi character recognition: a review
TL;DR: The main focus of this study is detailed survey of existing techniques for recognition of offline handwritten Hindi characters starting from database to various phases of character recognition.
References
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Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.