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Speeding up convolutional neural networks with low rank expansions

Abstract:

The focus of this paper is speeding up the application of convolutional neural networks. While delivering impressive results across a range of computer vision and machine learning tasks, these networks are computationally demanding, limiting their deployability. Convolutional layers generally consume the bulk of the processing time, and so in this work we present two simple schemes for drastically speeding up these layers. This is achieved by exploiting cross-channel or filter redundancy to c...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Oxford college:
New College
Role:
Author
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Institution:
University of Oxford
Oxford college:
Brasenose College
Role:
Author
Publisher:
British Machine Vision Association
Host title:
BMVC 2014 - Proceedings of the British Machine Vision Conference 2014
Journal:
BMVC 2014 - Proceedings of the British Machine Vision Conference 2014 More from this journal
Pages:
1-13
Publication date:
2014-01-01
Keywords:
Pubs id:
pubs:502590
UUID:
uuid:cdeb70cb-0b3f-4e99-9e9c-2ce50f8bb8f8
Local pid:
pubs:502590
Source identifiers:
502590
Deposit date:
2017-02-09

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