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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The Fusion of Deep Learning and Fuzzy Systems: A State-of-the-Art Survey
TL;DR: Fuzzy systems can not only depict uncertain and vague concepts widely existing in the real world, but also improve the prediction accuracy in deep learning models as mentioned in this paper , thus, it is important and necessary to go through the recent contributions about the fusion of deep learning and fuzzy systems.
Journal ArticleDOI
Green Learning: Introduction, Examples and Outlook
C.-C. Jay Kuo,Azad M. Madni +1 more
TL;DR: This paper offers an introduction to GL, its demonstrated applications, and future outlook, and sees a few successful GL examples with performance comparable with state-of-the-art DL solutions.
Journal ArticleDOI
Galaxy Image Classification Based on Citizen Science Data: A Comparative Study
TL;DR: Experiments reveal that autoencoders greatly speed up feature extraction in comparison with WND-CHARM and both classification strategies, either using convolutional neural networks or feature extraction, reach comparable accuracy.
Journal ArticleDOI
Chest X-ray image denoising method based on deep convolution neural network
TL;DR: The authors utilise batch normalisation to solve the problem of performance degradation due to the increase of neural network layers, and use residual learning of the distribution of noise in noisy X-ray images to accelerate the convergence speed of network model, shorten the training time, and improve accuracy of the model.
Journal ArticleDOI
Burns Depth Assessment Using Deep Learning Features
Aliyu Abubakar,Aliyu Abubakar,Hassan Ugail,Kirsty M. Smith,Kirsty M. Smith,Ali Maina Bukar,Ali Elmahmudi +6 more
TL;DR: The proposed pipeline achieved a state-of-the-art prediction accuracy and interestingly indicates that decision can be made in less than a minute whether the injury requires surgical intervention such as skin grafting or not.
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.