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Addressing appearance change in outdoor robotics with adversarial domain adaptation

Abstract:

Appearance changes due to weather and seasonal conditions represent a strong impediment to the robust implementation of machine learning systems in outdoor robotics. While supervised learning optimises a model for the training domain, it will deliver degraded performance in application domains that underlie distributional shifts caused by these changes. Traditionally, this problem has been addressed via the collection of labelled data in multiple domains or by imposing priors on the type of s...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/IROS.2017.8205961

Authors


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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
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
Hans-Lenze-Foundation More from this funder
Publisher:
Institute of Electrical and Electronics Engineers Publisher's website
Journal:
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017) Journal website
Host title:
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017)
Publication date:
2017-12-01
Acceptance date:
2017-06-29
DOI:
Source identifiers:
820394
Pubs id:
pubs:820394
UUID:
uuid:a7ef3081-6912-4fab-a9b2-922f7fe8d733
Local pid:
pubs:820394
Deposit date:
2018-01-22

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