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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
Version:
Accepted manuscript

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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 Division
Department:
Engineering Science
Oxford college:
St Annes College
Role:
Author
Publisher:
Springer Publisher's website
Publication date:
2018-10-06
Acceptance date:
2018-07-03
DOI:
ISSN:
0302-9743 and 1611-3349
Pubs id:
pubs:938457
URN:
uri:3be8efd8-cb06-458e-9f4b-62dd2d0693a9
UUID:
uuid:3be8efd8-cb06-458e-9f4b-62dd2d0693a9
Local pid:
pubs:938457
ISBN:
9783030012601

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