2015 GoingDeeperWithConvolutions

• (Szegedy et al., 2015) ⇒ Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. (2015). “Going Deeper With Convolutions.” In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Subject Headings: ILSVRC-2014 Task, Inception Architecture.

Notes

• http://github.com/google/inception/blob/master/inception.ipynb

Cited By

• http://scholar.google.com/scholar?q=%222015%22+Going+Deeper+With+Convolutions

Quotes

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 (ILSVRC2014). 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 of this architecture, GoogLeNet, a 22 layers deep network, was used to assess its quality in the context of object detection and classification.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 GoingDeeperWithConvolutions	Wei Liu Dumitru Erhan Pierre Sermanet Christian Szegedy Yangqing Jia Scott Reed Dragomir Anguelov Vincent Vanhoucke Andrew Rabinovich			Going Deeper With Convolutions