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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Unsupervised deep transfer learning with moment matching: A new intelligent fault diagnosis approach for bearings
TL;DR: An unsupervised deep transfer network with moment matching (UDTN-MM) is proposed, aiming to realize fault diagnosis under different working conditions, and shows that the approach is competitive on unlabeled samples in terms of diverse rotating speeds and fault severities.
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Decision Support Tools, Systems, and Artificial Intelligence in Cardiac Imaging
TL;DR: The past decades witnessed the development and integration of these tools, which can assist physicians with image interpretation, to optimize image quality for better visualization and accompany all imaging modalities.
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Remaining Useful Life Prognosis for Turbofan Engine Using Explainable Deep Neural Networks with Dimensionality Reduction.
TL;DR: The experimental results demonstrate the high accuracy and efficiency of the proposed model with dimensionality reduction and show that SHAP enhances the explainability in a conventional deep learning model for system prognosis.
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Graph Convolutional Subspace Clustering: A Robust Subspace Clustering Framework for Hyperspectral Image
TL;DR: This article revisits the subspace clustering with graph convolution and presents a novel sub space clustering framework calledgraph convolutional subspace clusters (GCSC) for robust HSI clustering, which recasts the self-expressiveness property of the data into the non-Euclidean domain, which results in a more robust graph embedding dictionary.
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Quantitative and qualitative VIS-NIR models for early determination of internal browning in ‘Cripps Pink’ apples during cold storage
TL;DR: In this article, the authors used semi-transmittance spectra to predict internal browning defect quantitatively and qualitatively in apple, by a non-destructive equipment from spectra collected before the disorder develops.
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.