Deep Residual Learning for Image Recognition

Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of l...

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Bibliographic Details

Published in:	2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 770 - 778
Main Authors:	Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.06.2016
Subjects:	Complexity theory Degradation Image recognition Image segmentation Neural networks Training Visualization
ISSN:	1063-6919
Online Access:	Get full text
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