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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Deep Focus Parallel Convolutional Neural Network for Imbalanced Classification of Machinery Fault Diagnostics
TL;DR: The diagnostics results demonstrate that DFPCN outperforms the state-of-the-art CNN-based methods in terms of accuracy and stability, and avoids adding computational burden with the redundant samples when compared with oversampling methods.
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An efficient data model for energy prediction using wireless sensors
TL;DR: This paper proposes a system based on Multilayer Perceptron (MLP) to predict energy consumption of a building using collected information from a Wireless Sensor Network (WSN) and achieves state-of-the-art results.
Journal ArticleDOI
Detection of broadleaf weeds growing in turfgrass with convolutional neural networks
TL;DR: Deep learning CNN (DL-CNN) models that are remarkably accurate at detection of broadleaf weeds in turfgrasses using in situ video input in conjunction with a smart sprayer are reported.
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BSNet: Bi-Similarity Network for Few-shot Fine-grained Image Classification
TL;DR: Li et al. as mentioned in this paper proposed a Bi-Similarity Network (BSNet) which consists of a single embedding module and a bi-similarity module of two similarity measures.
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Bioinspired Computational Intelligence and Transportation Systems: A Long Road Ahead
TL;DR: This paper comprehensively reviews the state-of-the-art around the application of bioinspired methods to the challenges arising in the broad field of intelligent transportation system (ITS), complemented by an initiatory taxonomic introduction to bioinspired computational intelligence.
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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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.