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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Journal ArticleDOI
STDnet-ST: Spatio-temporal ConvNet for small object detection
TL;DR: STDnet-ST as mentioned in this paper is an end-to-end spatio-temporal convolutional neural network for small object detection in video, which detects small objects over time and correlates pairs of the top-ranked regions with the highest likelihood of containing those small objects.
Journal ArticleDOI
FQTSFM: A fuzzy-quantum time series forecasting model
Pritpal Singh,Pritpal Singh +1 more
TL;DR: A hybrid model called fuzzy-quantum time series forecasting model (FQTSFM) is designed, which converges very fast compared to the existing hybridized based FTS models and is able to evolve one-step ahead forecasted results.
Journal ArticleDOI
Learning graph structure via graph convolutional networks
TL;DR: A method that learns Graph Structure via graph Convolutional Networks (GSCN), which introduces the graph structure parameters measuring the correlation degrees of adjacent nodes, which is better to handle the graph-structured data of non-stationarity.
Journal ArticleDOI
STAN: A sequential transformation attention-based network for scene text recognition
TL;DR: A Sequential Transformation Attention-based Network (STAN), which comprises a sequential transformation network and an attention-based recognition network, is proposed for general scene text recognition.
Proceedings ArticleDOI
Deep Learning Techniques for Cyber Security Intrusion Detection : A Detailed Analysis
TL;DR: This study uses the CSE-CIC-IDS 2018 dataset and TensorFlow system as the benchmark dataset and software library in intrusion detection experiments and uses the most important performance indicators, namely, accuracy, detection rate, and false alarm rate for evaluating the efficiency of several methods.
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
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Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
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.