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Incremental adversarial domain adaptation for continually changing environments

Abstract:

Continuous appearance shifts such as changes in weather and lighting conditions can impact the performance of deployed machine learning models. While unsupervised domain adaptation aims to address this challenge, current approaches do not utilise the continuity of the occurring shifts. In particular, many robotics applications exhibit these conditions and thus facilitate the potential to incrementally adapt a learnt model over minor shifts which integrate to massive differences over time. Our...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/ICRA.2018.8460982

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More by this author
Institution:
University of Oxford
Division:
MPLS division
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
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
Pembroke College
Role:
Author
Publisher:
IEEE Publisher's website
Journal:
2018 IEEE International Conference on Robotics and Automation Journal website
Host title:
2018 IEEE International Conference on Robotics and Automation
Publication date:
2018-09-13
Acceptance date:
2017-12-01
DOI:
ISSN:
1050-4729
Source identifiers:
930620
Pubs id:
pubs:930620
UUID:
uuid:5c093a46-126e-446a-a558-167af6e0fb83
Local pid:
pubs:930620
Deposit date:
2019-01-25

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