Conference item
Multi-task learning using uncertainty to weigh losses for scene geometry and semantics
- Abstract:
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Numerous deep learning applications benefit from multitask learning with multiple regression and classification objectives. In this paper we make the observation that the performance of such systems is strongly dependent on the relative weighting between each task's loss. Tuning these weights by hand is a difficult and expensive process, making multi-task learning prohibitive in practice. We propose a principled approach to multi-task deep learning which weighs multiple loss functions by cons...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
-
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(Accepted manuscript, 4.3MB)
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- Publisher copy:
- 10.1109/cvpr.2018.00781
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Authors
Bibliographic Details
- Publisher:
- IEEE Publisher's website
- Pages:
- 7482-7491
- Host title:
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publication date:
- 2018-12-17
- Event title:
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018)
- Event location:
- Salt Lake City, Utah, USA
- Event website:
- http://cvpr2018.thecvf.com/
- Event start date:
- 2018-06-18T00:00:00Z
- Event end date:
- 2018-06-22T00:00:00Z
- DOI:
- EISSN:
-
2575-7075
- ISSN:
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1063-6919
Item Description
- Language:
- English
- Keywords:
- Pubs id:
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1105069
- Local pid:
- pubs:1105069
- Deposit date:
- 2020-05-15
Terms of use
- Copyright holder:
- Institute of Electrical and Electronics Engineers
- Copyright date:
- 2018
- Rights statement:
- © 2018 IEEE.
- Notes:
- This paper was presented at the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018), Salt Lake City, Utah, USA, June 2018. This is the accepted manuscript version of the article. The final version is available online from the Institute of Electrical and Electronics Engineers at: https://doi.org/10.1109/CVPR.2018.00781
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