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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Proceedings ArticleDOI
A Performance Evaluation of Convolutional Neural Networks for Face Anti Spoofing
Chaitanya Nagpal,Shiv Ram Dubey +1 more
TL;DR: A performance evaluation of CNNs for face anti-spoofing using the Inception and ResNet CNN architectures is done and favorable results are obtained.
Journal ArticleDOI
Ensemble deep learning for automated visual classification using EEG signals
TL;DR: An automated visual classification framework in which a novel analysis method (LSTMS-B) of EEG signals guides the selection of multiple networks that leads to the improvement of classification performance is proposed.
Journal ArticleDOI
Local Levenberg-Marquardt Algorithm for Learning Feedforwad Neural Networks
TL;DR: The paper shows that the local modification of the Levenberg-Marquardt algorithm significantly improves the algorithm’s performance for bigger networks.
Journal ArticleDOI
Designing Future Precision Agriculture: Detection of Seeds Germination Using Artificial Intelligence on a Low-Power Embedded System
TL;DR: A Convolutional Neural Network is designed which achieves 83% of average Intersection over Union (IoU) score on the test dataset and 97% of seeds recognition accuracy on the validation dataset and demonstrates that the proposed system opens up wide vista for smart applications in the context of Internet of Things requiring the intelligent and autonomous operation from ‘things’.
Proceedings ArticleDOI
Dilated Residual Network with Multi-head Self-attention for Speech Emotion Recognition
TL;DR: This paper has proposed the combining use of Dilated Residual Network (DRN) and Multi-head Self-attention to alleviate the above limitations in speech emotion recognition.
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
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.
Journal ArticleDOI
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.