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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Veröffentlicht in:	2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) S. 770 - 778
Hauptverfasser:	Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.06.2016
Schlagworte:	Complexity theory Degradation Image recognition Image segmentation Neural networks Training Visualization
ISSN:	1063-6919
Online-Zugang:	Volltext
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