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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Deep Learning in Plant Phenological Research: A Systematic Literature Review
TL;DR: In this article , the authors present a systematic literature review aiming to thoroughly analyze all primary studies on deep learning approaches in plant phenology research, and identify and discuss research trends and highlight promising future directions.
Journal ArticleDOI
Noise-reducing attention cross fusion learning transformer for histological image classification of osteosarcoma
Liangrui Pan,Hetian Wang,Lianli Wang,Boya Ji,Mingting Liu,Mitchai Chongcheawchamnan,Yuan Jin,Shaoliang Peng +7 more
TL;DR: Wang et al. as discussed by the authors proposed a typical transformer image classification framework by integrating noise reduction convolutional autoencoder and feature cross fusion learning (NRCA-FCFL) to classify osteosarcoma histological images.
Journal ArticleDOI
Artificial Intelligence in Optical Communications: From Machine Learning to Deep Learning
Danshi Wang,Min Zhang +1 more
TL;DR: In this article, the authors focus on the state-of-the-art DL algorithms and aims at highlighting the contributions of DL to optical communications and propose a data-driven channel modeling method to improve the end-to-end learning performance.
Proceedings ArticleDOI
Fast object detection in compressed JPEG Images
TL;DR: This paper modify the well-known Single Shot multibox Detector by replacing its first layers with one convolutional layer dedicated to process the DCT inputs, and proposes a fast deep architecture for object detection in JPEG images, one of the most widespread compression format.
Journal ArticleDOI
Complete set of translation invariant measurements with Lipschitz bounds
TL;DR: In this article, the authors construct low dimensional representations of signals in C n that are invariant under finite unitary group actions, as a special case they establish the existence of low-dimensional and complete Z m -invariant representations for any m ∈ N.
References
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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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Diederik P. Kingma,Jimmy Ba +1 more
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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.