2015 GoingDeeperWithConvolutions

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Subject Headings: ILSVRC-2014 Task, Inception Architecture.

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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.

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2015 GoingDeeperWithConvolutionsWei Liu
Dumitru Erhan
Pierre Sermanet
Christian Szegedy
Yangqing Jia
Scott Reed
Dragomir Anguelov
Vincent Vanhoucke
Andrew Rabinovich
Going Deeper With Convolutions