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
Deep learning based image classification for intestinal hemorrhage
TL;DR: A supervised learning ensemble to detect the bleeding in the images of Wireless Capsule Endoscopy accurately finds out the best possible combination of attributes required to classify bleeding symptoms in endoscopy images.
Journal ArticleDOI
Deep conditional adaptation networks and label correlation transfer for unsupervised domain adaptation
TL;DR: A conditional adaptation strategy is presented to reduce the domain distribution discrepancy and to address category mismatch and class prior bias, which are usually ignored in marginal adaptation approaches, and a label correlation transfer algorithm is proposed to address the unsupervised issues.
Journal ArticleDOI
A novel learning-based global path planning algorithm for planetary rovers
TL;DR: In this paper, a deep convolutional neural network with dual branches (DB-CNN) is designed and trained, which can plan path directly from orbital images of planetary surfaces without implementing environment mapping.
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Deep-learning-based porosity monitoring of laser welding process
TL;DR: In this paper, a CNN-based monitoring model achieved a classification accuracy of 96.1% for porosity occurrence detection, though the prediction of micro (less than 100 µm) and deep subsurface pores still remains challenging.
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Deep Cytometry: Deep learning with Real-time Inference in Cell Sorting and Flow Cytometry.
Yueqin Li,Ata Mahjoubfar,Ata Mahjoubfar,Claire Lifan Chen,Claire Lifan Chen,Kayvan Reza Niazi,Li Pei,Bahram Jalali +7 more
TL;DR: In this article, a convolutional neural network (CNN) was used to classify white blood cells and epithelial cancer cells with more than 95% accuracy in a label-free fashion.
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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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.