Going deeper with convolutions

Christian Szegedy, Wei Liu, Yangqing Jia, P. Sermanet, Scott E. Reed, Dragomir Anguelov et al.

2015 Computer Vision and Pattern Recognition Cited 46,871 times

Abstract

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 computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.

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BibTeX

@inproceedings{Szegedy2015,
  author = {Szegedy, Christian and Liu, Wei and Jia, Yangqing and Sermanet, Pierre and Reed, Scott and Anguelov, Dragomir and Erhan, Dumitru and Vanhoucke, Vincent and Rabinovich, Andrew},
  booktitle = {Proceedings of the IEEE conference on computer vision and pattern recognition},
  title = {Going deeper with convolutions},
  year = {2015},
  pages = {1–9},
}