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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Journal ArticleDOI
Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Images
Hakan Açikgöz,Deniz Korkmaz +1 more
TL;DR: An efficient convolutional neural network model is proposed for fast and accurate detection and classification of faults in PV module cells with SqueezeNet, which has fewer parameters and model size using the transfer learning approach.
Journal ArticleDOI
Cervical Cancer Classification Using Combined Machine Learning and Deep Learning Approach
Hiam Alquran,Wan Azani Mustafa,Isam Abu Qasmieh,Yasmeen Mohd Yacob,Mohammed Alsalatie,Yazan A. Al-Issa,Ali Mohammad Alqudah +6 more
TL;DR: In this article , the authors used ResNet101 features to discriminate seven classes of images in Support Vector Machine (SVM) classifier and achieved an accuracy of 97.3%.
Journal ArticleDOI
Sustainable Human–Robot Collaboration Based on Human Intention Classification
TL;DR: A deep learning algorithm is used to classify muscular signals of human motions with accuracy of 88%.
Journal ArticleDOI
Machine learning techniques for identification of carcinogenic mutations, which cause breast adenocarcinoma
TL;DR: In this paper , the authors developed a framework for the early detection of breast adenocarcinoma using machine learning techniques, such as decision tree, random forest, and Gaussian Naïve Bayes.
Proceedings ArticleDOI
Stock Movement Prediction and Portfolio Management via Multimodal Learning with Transformer
Divyanshu Daiya,Che Lin +1 more
TL;DR: In this paper, a multimodal architecture using dilated causal convolutions and Transformer blocks for feature extraction from financial indicators and news data is proposed for stock movement prediction, which provides a significant improvement from 74.29% to 77.74%.
References
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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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Diederik P. Kingma,Jimmy Ba +1 more
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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.
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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.