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
More filters
Proceedings ArticleDOI
Train Once, Locate Anytime for Anyone: Adversarial Learning based Wireless Localization
TL;DR: In this paper, the authors proposed iToLoc, a deep learning based localization system that achieves high localization accuracy, low maintenance cost, and ubiquity simultaneously, without relying on specific infrastructures.
Journal ArticleDOI
Freeway accident detection and classification based on the multi-vehicle trajectory data and deep learning model
TL;DR: A Deep Convolutional Neural Network model is developed to recognize an accident from the normal driving of vehicles and also identify the type of the accident, and the six types of traffic accidents are considered in this study.
Journal ArticleDOI
Automated Diagnosis of Chest X-Ray for Early Detection of COVID-19 Disease.
Ebrahim Mohammed Senan,Ali Alzahrani,Mohammed Alzahrani,Nizar Alsharif,Theyazn H. H. Aldhyani +4 more
TL;DR: In this paper, two models of deep learning, ResNet-50 and AlexNet, were introduced to diagnose X-ray datasets collected from many sources, and each network diagnosed a multiclass (four classes) and a two-class dataset.
Journal ArticleDOI
Face Gender Recognition in the Wild: An Extensive Performance Comparison of Deep-Learned, Hand-Crafted, and Fused Features with Deep and Traditional Models
TL;DR: This work performs a comprehensive comparative study to analyze the classification performance of two widely used learning models, i.e., CNN and SVM, when they are combined with seven features that include hand-crafted, deep-learned, and fused features.
Journal ArticleDOI
Attend and Guide (AG-Net): A Keypoints-driven Attention-based Deep Network for Image Recognition
TL;DR: Zhang et al. as discussed by the authors proposed an end-to-end CNN model, which learns meaningful features linking fine-grained changes using a keypoints-based attention mechanism for visual recognition in still images.
References
More filters
Proceedings ArticleDOI
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.
Journal ArticleDOI
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.
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.