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Open AccessJournal ArticleDOI

Recent advances in convolutional neural networks

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.

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Citations
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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.

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
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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

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

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

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.
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