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
Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses
TL;DR: In this article, an efficient approach is proposed to generate gradient-based attacks that induce misclassifications with low L2 norm, by decoupling the direction and the norm of the adversarial perturbation that is added to the image.
Journal ArticleDOI
Deep Multimodal Feature Analysis for Action Recognition in RGB+D Videos
TL;DR: A new deep autoencoder based shared-specific feature factorization network to separate input multimodal signals into a hierarchy of components and a structured sparsity learning machine is proposed which utilizes mixed norms to apply regularization within components and group selection between them for better classification performance.
Journal ArticleDOI
Generative adversarial networks and convolutional neural networks based weather classification model for day ahead short-term photovoltaic power forecasting
Fei Wang,Zhanyao Zhang,Chun Liu,Yili Yu,Songling Pang,Neven Duić,Miadreza Shafie-khah,Joao P. S. Catalao,Joao P. S. Catalao,Joao P. S. Catalao +9 more
TL;DR: The simulation results illustrate that generative adversarial networks can generate new samples with high quality that capture the intrinsic features of the original data, but not to simply memorize the training data, and convolutional neural networks classification models show better classification performance than traditional machine learning models.
Journal ArticleDOI
Seismic Waveform Classification and First-Break Picking Using Convolution Neural Networks
TL;DR: This letter investigates the application of CNNs for classifying time-space waveforms from seismic shot gathers and picking FBs of both direct wave and refracted wave and illustrates that CNN is an efficient automatic data-driven classifier and picker.
Journal ArticleDOI
DCNN-Based Multi-Signal Induction Motor Fault Diagnosis
TL;DR: A DL-based multi-signal fault diagnosis method that leverages the powerful feature learning ability of a convolutional neural network in images to achieve robust performance and demonstrate effectiveness in induction motor application is proposed.
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.