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
DeepVisage: Making Face Recognition Simple Yet With Powerful Generalization Skills
TL;DR: Zhang et al. as discussed by the authors proposed an easily trainable and single CNN-based face recognition method, which exploits the residual learning framework to compute the loss and obtained very competitive and state-of-the-art results on the LFW, IJB-A, YouTube faces and CACD datasets.
Journal ArticleDOI
A Review on Machine Learning, Artificial Intelligence, and Smart Technology in Water Treatment and Monitoring
TL;DR: This review offers a cross-section of peer reviewed, critical water-based applications that have been coupled with AI or ML, including chlorination, adsorption, membrane filtration, water-quality-index monitoring,Water- quality-parameter modeling, river-level monitoring, and aquaponics/hydroponics automation/monitoring.
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Automatic Diabetic Retinopathy Diagnosis Using Adaptive Fine-Tuned Convolutional Neural Network
TL;DR: In this article, a two-stage transfer learning method was employed for automatic diabetic retinopathy (DR) screening on fundus images, where the first layer of a pre-trained CNN model was re-initialized and the model was fine-tuned, such that the low-level layers learned the local structures of the lesion and normal regions.
Journal ArticleDOI
Energy Demand Forecasting Using Deep Learning: Applications for the French Grid
TL;DR: This paper proposes a mixed architecture consisting of a convolutional neural network coupled with an artificial neural network that obtains the highest performance score when compared with other alternatives, including Autoregressive Integrated Moving Average (ARIMA) and traditional ANN models.
Journal ArticleDOI
Visual SLAM for robot navigation in healthcare facility
TL;DR: Wang et al. as mentioned in this paper proposed a novel SLAM technology using RGB and depth images to improve hospital operation efficiency, reduce the risk of doctor-patient cross-infection, and curb the spread of the COVID-19 pandemic.
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.
Proceedings Article
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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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.