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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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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
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Name:
Engineering & Physical Sciences Research Council
Grant:
EP/M019918/1
Publisher:
Institute of Electrical and Electronics Engineers
Host title:
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017)
Journal:
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017) More from this journal
Publication date:
2017-12-01
Acceptance date:
2017-06-29
DOI:
Pubs id:
pubs:820394
UUID:
uuid:a7ef3081-6912-4fab-a9b2-922f7fe8d733
Local pid:
pubs:820394
Source identifiers:
820394
Deposit date:
2018-01-22

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