Going deeper with convolutions

We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the...

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Veröffentlicht in:	2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) S. 1 - 9
Hauptverfasser:	Szegedy, Christian, Wei Liu, Yangqing Jia, Sermanet, Pierre, Reed, Scott, Anguelov, Dragomir, Erhan, Dumitru, Vanhoucke, Vincent, Rabinovich, Andrew
Format:	Tagungsbericht Journal Article
Sprache:	Englisch
Veröffentlicht:	IEEE 01.06.2015
Schlagworte:	Architecture (computers) Classification Computation Computer architecture Computer vision Conferences Constants Convolutional codes Networks Neural networks Object detection Pattern recognition Sparse matrices Visualization
ISSN:	1063-6919, 1063-6919
Online-Zugang:	Volltext
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