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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.
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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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Journal ArticleDOI

Sensor Fusion Basketball Shooting Posture Recognition System Based on CNN

TL;DR: The system, using the sensor fusion framework, collected the basketball shooting posture data of the players’ main force hand and main force foot for sensor fusion and used a deep learning model based on convolutional neural networks for recognition.
Journal ArticleDOI

Convolutional Networks Outperform Linear Decoders in Predicting EMG From Spinal Cord Signals

TL;DR: Forelimb electromyography signals were reconstructed from multi-unit neural signals recorded with multiple electrode arrays from the corticospinal tract in rats to suggest that the CNN model implicitly predicted short-term dynamics of skilled forelimb movements from neural signals.
Journal ArticleDOI

Pseudo-colored rate map representation for speech emotion recognition

TL;DR: In this article, log-power rate map features are suggested as an auditory model for the speech emotion recognition task, and a threshold function is used to focus on regions with high spectral energy.
Journal ArticleDOI

Learning quantum data with the quantum earth mover’s distance

TL;DR: In this paper , the authors proposed a quantum Wasserstein generative adversarial network (qWGAN) which takes advantage of the quantum earth mover's (EM) distance and provides an efficient means of performing learning on quantum data.
Proceedings ArticleDOI

Moving object detection and tracking using deep learning neural network and correlation filter

TL;DR: Gaussian mixture model (GMM) based object detection, deep learning neural network-based recognition and tracking of objects using correlation filter is proposed, which can handle false detections, with improving the efficiency.
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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