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AutoNovel: automatically discovering and learning novel visual categories

Abstract:

We tackle the problem of discovering novel classes in an image collection given labelled examples of other classes. We present a new approach called AutoNovel to address this problem by combining three ideas: (1) we suggest that the common approach of bootstrapping an image representation using the labeled data only introduces an unwanted bias, and that this can be avoided by using self-supervised learning to train the representation from scratch on the union of labelled and unlabelled data; ...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/tpami.2021.3091944

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0002-7995-9999
More by this author
Institution:
University of Oxford
Department:
ENGINEERING SCIENCE
Sub department:
Engineering Science
Oxford college:
Brasenose College
Role:
Author
ORCID:
0000-0002-8945-8573
Publisher:
IEEE Publisher's website
Journal:
IEEE Transactions on Pattern Analysis and Machine Intelligence Journal website
Publication date:
2021-06-24
Acceptance date:
2021-06-13
DOI:
EISSN:
1939-3539
ISSN:
0162-8828
Pmid:
34166184
Language:
English
Keywords:
Pubs id:
1184922
Local pid:
pubs:1184922
Deposit date:
2021-08-10

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