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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Journal ArticleDOI
Toward Achieving Robust Low-Level and High-Level Scene Parsing
TL;DR: The segmentation network enhanced fully convolutional network (EFCN) is named based on its significantly enhanced structure over FCN and achieves state-of-the-arts on segmentation datasets of ADE20K, Pascal Context, SUN-RGBD, and Pascal VOC 2012.
Journal ArticleDOI
A 28-nm-CMOS Based 145-GHz FMCW Radar: System, Circuits, and Characterization
Akshay Visweswaran,Kristof Vaesen,Miguel Glassee,Anirudh Kankuppe,Siddhartha Sinha,Claude Desset,Thomas Gielen,Andre Bourdoux,Piet Wambacq +8 more
TL;DR: Extensive characterization results showcase state-of-the-art performance of the TRXs, while the code-domain multiple-input and multiple-output (MIMO) radars built with them demonstrate vital-sign and gesture detections.
Journal ArticleDOI
Knowledge-based radiation treatment planning: A data-driven method survey.
Shadab Momin,Yabo Fu,Yang Lei,Justin Roper,Jeffrey D. Bradley,Walter J. Curran,Tian Liu,Xiaofeng Yang +7 more
TL;DR: This paper surveys the data-driven dose prediction methods investigated for knowledge-based planning (KBP) in the last decade, classified into two major categories-traditional KBP methods and deep-learning methods-according to their techniques of utilizing previous knowledge.
Journal ArticleDOI
Fault Diagnosis of Motor Bearings Based on a One-Dimensional Fusion Neural Network.
TL;DR: Experimental results show that the proposed one-dimensional fusion neural network (OFNN) can effectively enhance the cross-domain adaptive ability of the model and has a better diagnostic accuracy than other existing experimental methods.
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3D Hand Pose Estimation Using Synthetic Data and Weakly Labeled RGB Images
TL;DR: A weakly-supervised method, adaptating from fully-annotated synthetic dataset toWeakly-labeled real-world single RGB dataset with the aid of a depth regularizer, which serves as weak supervision for 3D pose prediction, which proves the effectiveness of the proposed depthRegularizer and the CVAE-based framework.
References
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Gradient-based learning applied to document recognition
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