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ImageNet classification with deep convolutional neural networks

Authors:

Alex Krizhevsky,

Ilya Sutskever,

Geoffrey E. HintonAuthors Info & Claims

Communications of the ACM, Volume 60, Issue 6

Pages 84 - 90

https://doi.org/10.1145/3065386

Published: 24 May 2017 Publication History

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Abstract

We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry.

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ImageNet classification with deep convolutional neural networks

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Published In

cover image Communications of the ACM

Communications of the ACM Volume 60, Issue 6

June 2017

93 pages

ISSN:0001-0782

EISSN:1557-7317

DOI:10.1145/3098997

Editor:
Moshe Y. Vardi
Association for Computing Machinery, New York, NY

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Copyright © 2017 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 24 May 2017

Published in CACM Volume 60, Issue 6

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