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Learning multiple visual domains with residual adapters

Abstract:

There is a growing interest in learning data representations that work well for many different types of problems and data. In this paper, we look in particular at the task of learning a single visual representation that can be successfully utilized in the analysis of very different types of images, from dog breeds to stop signs and digits. Inspired by recent work on learning networks that predict the parameters of another, we develop a tunable deep network architecture that, by means of adapt...

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Publication status:
Published
Peer review status:
Peer reviewed

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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
Department:
Engineering Science
Oxford college:
New College
Role:
Author
Dental Technologists Association More from this funder
Publisher:
Neural Information Processing Systems Publisher's website
Journal:
Advances in Neural Information Processing Systems Journal website
Host title:
Thirty-first Annual Conference on Neural Information Processing Systems (NIPS 2017)
Publication date:
2018-06-01
Acceptance date:
2017-09-04
ISSN:
1049-5258
Source identifiers:
853791
Pubs id:
pubs:853791
UUID:
uuid:0e03cb1d-13ba-4afa-afc6-6b90a35c97b8
Local pid:
pubs:853791
Deposit date:
2018-06-26

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