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Interpolating convolutional neural networks using batch normalization

Abstract:

Perceiving a visual concept as a mixture of learned ones is natural for humans, aiding them to grasp new concepts and strengthening old ones. For all their power and recent success, deep convolutional networks do not have this ability. Inspired by recent work on universal representations for neural networks, we propose a simple emulation of this mechanism by purposing batch normalization layers to discriminate visual classes, and formulating a way to combine them to solve new tasks. We show t...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/978-3-030-01261-8_35

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
St Anne's College
Role:
Author
Publisher:
Springer Publisher's website
Journal:
15th European Conference on Computer Vision (ECCV 2018) Journal website
Host title:
15th European Conference on Computer Vision (ECCV 2018)
Publication date:
2018-10-06
Acceptance date:
2018-07-03
DOI:
ISSN:
1611-3349 and 0302-9743
Source identifiers:
938457
ISBN:
9783030012601
Keywords:
Pubs id:
pubs:938457
UUID:
uuid:3be8efd8-cb06-458e-9f4b-62dd2d0693a9
Local pid:
pubs:938457
Deposit date:
2018-11-08

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