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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Skill transfer support model based on deep learning
TL;DR: The present study facilitates skill transfer in manufacturing systems by adapting or learning new skills for junior operators by integrating two deep learning models that can simultaneously recognize the action and detect the object.
Journal ArticleDOI
A Contactless Respiratory Rate Estimation Method Using a Hermite Magnification Technique and Convolutional Neural Networks
TL;DR: A new non-contact strategy to estimate respiratory rate based on Eulerian motion video magnification technique using Hermite transform and a system based on a Convolutional Neural Network (CNN).
Journal ArticleDOI
Enhancement of Deep Learning in Image Classification Performance Using Xception with the Swish Activation Function for Colorectal Polyp Preliminary Screening
TL;DR: The results indicate that the proposed model can enhance the original convolutional neural network model with evaluation classification performance by achieving accuracy of up to 98.99% for classifying into two classes and 91.48% for three classes.
Journal ArticleDOI
Benchmarking performance of machine and deep learning-based methodologies for Urdu text document classification
Muhammad Nabeel Asim,Muhammad Nabeel Asim,Muhammad Nabeel Asim,Muhammad Usman Ghani,Muhammad Ali Ibrahim,Muhammad Ali Ibrahim,Waqar Mahmood,Andreas Dengel,Andreas Dengel,Sheraz Ahmed +9 more
TL;DR: In this article, the authors provided a publicly available benchmark dataset for Urdu text document classification and evaluated the performance of various deep learning-based methodologies for text document classifier.
Journal ArticleDOI
Breast Tumor Classification in Ultrasound Images Using Combined Deep and Handcrafted Features
Mohammad I. Daoud,Samir Abdel-Rahman,Tariq M. Bdair,Mahasen Al-Najar,Feras Al-Hawari,Rami Alazrai +5 more
TL;DR: The results suggest that the combined CONV and morphological features can achieve effective breast ultrasound image classifications that increase the capability of detecting malignant tumors and reduce the potential of misclassifying benign tumors.
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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Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
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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.