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Learning deep visual object models from noisy web data: How to make it work

Abstract:

Deep networks thrive when trained on large scale data collections. This has given ImageNet a central role in the development of deep architectures for visual object classification. However, ImageNet was created during a specific period in time, and as such it is prone to aging, as well as dataset bias issues. Moving beyond fixed training datasets will lead to more robust visual systems, especially when deployed on robots in new environments which must train on the objects they encounter there...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/IROS.2017.8206444

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
Pembroke College
Role:
Author
ORCID:
0000-0002-7556-6098
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More from this funder
Grant:
637076 - RoboExNovo (BC, TT
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Grant:
the CHIST-ERAprojectALOOF(BC,NM,JY,NH
EUFP7600623STRANDS(JY,NH)
Publisher:
IEEE Publisher's website
Journal:
2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Journal website
Pages:
5564-5571
Publication date:
2017-12-14
Acceptance date:
2017-06-15
DOI:
ISSN:
2153-0866
Source identifiers:
821135
ISBN:
9781538626825
Pubs id:
pubs:821135
UUID:
uuid:03b960ec-4922-4d2e-8ea4-4705a572f2c8
Local pid:
pubs:821135
Deposit date:
2018-01-24

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