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Learning to discover novel visual categories via deep transfer clustering

Abstract:

We consider the problem of discovering novel object categories in an image collection. While these images are unlabelled, we also assume prior knowledge of related but different image classes. We use such prior knowledge to reduce the ambiguity of clustering, and improve the quality of the newly discovered classes. Our contributions are twofold. The first contribution is to extend Deep Embedded Clustering to a transfer learning setting; we also improve the algorithm by introducing a represent...

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Publication status:
Published
Peer review status:
Reviewed (other)

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Publisher copy:
10.1109/ICCV.2019.00849

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Sub department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Sub department:
Engineering Science
Role:
Author
ORCID:
0000-0002-8945-8573
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
Brasenose College
Role:
Author
ORCID:
0000-0002-8945-8573
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Name:
Engineering & Physical Sciences Research Council
Grant:
EP/M013774/1
Publisher:
IEEE
Host title:
IEEE ICCV
Journal:
IEEE ICCV 2019 More from this journal
Publication date:
2020-02-27
Acceptance date:
2019-07-22
DOI:
Language:
English
Keywords:
Pubs id:
pubs:1048556
UUID:
uuid:a7e7bc2a-d3cb-457e-906a-8001012725ae
Local pid:
pubs:1048556
Source identifiers:
1048556
Deposit date:
2019-09-02

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