- Abstract:
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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...
Expand abstract - Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Accepted manuscript
- Files:
-
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(pdf, 1.5MB)
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- Publisher copy:
- 10.1109/IROS.2017.8205961
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- Publisher:
- Institute of Electrical and Electronics Engineers Publisher's website
- Publication date:
- 2017-12-05
- Acceptance date:
- 2017-06-29
- DOI:
- Pubs id:
-
pubs:820394
- URN:
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uri:a7ef3081-6912-4fab-a9b2-922f7fe8d733
- UUID:
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uuid:a7ef3081-6912-4fab-a9b2-922f7fe8d733
- Local pid:
- pubs:820394
- Copyright holder:
- Institute of Electrical and Electronics Engineers
- Copyright date:
- 2017
- Notes:
- © 2017 IEEE
Conference item
Addressing appearance change in outdoor robotics with adversarial domain adaptation
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