Conference item
Addressing appearance change in outdoor robotics with adversarial domain adaptation
- 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...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Accepted manuscript, pdf, 1.5MB)
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- Publisher copy:
- 10.1109/IROS.2017.8205961
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Authors
Funding
Hans-Lenze-Foundation
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Bibliographic Details
- 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:
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820394
Item Description
- Pubs id:
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pubs:820394
- UUID:
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uuid:a7ef3081-6912-4fab-a9b2-922f7fe8d733
- Local pid:
- pubs:820394
- Deposit date:
- 2018-01-22
Terms of use
- Copyright holder:
- Institute of Electrical and Electronics Engineers
- Copyright date:
- 2017
- Notes:
- © 2017 IEEE
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