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 detection of epileptic seizure based on approximate entropy, recurrence quantification analysis and convolutional neural networks.
TL;DR: Automatic detection of epileptic recordings become possible and it would be a valuable tool for the clinical diagnosis and treatment of epilepsy.
Journal ArticleDOI
Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects
TL;DR: In this article , a survey of state-of-the-art DL frameworks for hyperspectral imaging classification (HSIC) is presented. And the authors discuss some strategies to improve the generalization performance of DL strategies and provide some future guidelines.
Journal ArticleDOI
Fusion of Multiscale Convolutional Neural Networks for Building Extraction in Very High-Resolution Images
TL;DR: A novel parallel support vector mechanism (SVM)-based fusion strategy to take full use of deep features at different scales as extracted by the MCNN structure, which has demonstrated the superior performance of the proposed methodology in extracting complex buildings in urban districts.
Journal ArticleDOI
Diabetic retinopathy detection and stage classification in eye fundus images using active deep learning
TL;DR: In this article, an active deep learning (ADL-CNN) model was proposed to automatically extract features compare to handcrafted-based features for diabetic retinopathy (DR) screening.
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Geospatial Object Detection on High Resolution Remote Sensing Imagery Based on Double Multi-Scale Feature Pyramid Network
Xiaodong Zhang,Kun Zhu,Guanzhou Chen,Xiaoliang Tan,Zhang Lifei,Fan Dai,Puyun Liao,Yuanfu Gong +7 more
TL;DR: An effective region-based VHR remote sensing imagery object detection framework named Double Multi-scale Feature Pyramid Network (DM-FPN) was proposed in this paper, which utilizes inherent multi-scale pyramidal features and combines the strong-semantic, low-resolution features and the weak-Semantic, high-resolution Features simultaneously.
References
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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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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.