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Inductive visual localisation: Factorised training for superior generalisation

Abstract:

End-to-end training of Recurrent Neural Networks (RNNs) have been successfully applied to numerous problems that require processing sequences, such as image captioning, machine translation, and text recognition. However, RNNs often struggle to generalise to sequences longer than the ones encountered during training. In this work, we propose to optimize neural networks explicitly for induction. The idea is to first decompose the problem in a sequence of inductive steps and then to explicitly t...

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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
Oxford college:
Balliol College
Role:
Author
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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
New College
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
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Grant:
Clarendon Fund scholarship
Publisher:
British Machine Vision Conference Publisher's website
Journal:
British Machine Vision Conference Journal website
Host title:
British Machine Vision Conference
Publication date:
2018-09-03
Acceptance date:
2018-07-06
Source identifiers:
942829
Pubs id:
pubs:942829
UUID:
uuid:c110d333-44c3-4e77-ac0b-01259881dc61
Local pid:
pubs:942829
Deposit date:
2018-11-16

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