Multi-task learning using uncertainty to weigh losses for scene geometry and semantics
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 b...
| Main Authors: | , , |
|---|---|
| Format: | Conference item |
| Language: | English |
| Published: |
IEEE
2018
|
| _version_ | 1797063118655848448 |
|---|---|
| author | Cipolla, R Gal, Y Kendall, A |
| author_facet | Cipolla, R Gal, Y Kendall, A |
| author_sort | Cipolla, R |
| collection | OXFORD |
| description | 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 considering the homoscedastic uncertainty of each task. This allows us to simultaneously learn various quantities with different units or scales in both classification and regression settings. We demonstrate our model learning per-pixel depth regression, semantic and instance segmentation from a monocular input image. Perhaps surprisingly, we show our model can learn multi-task weightings and outperform separate models trained individually on each task. |
| first_indexed | 2024-03-06T20:55:16Z |
| format | Conference item |
| id | oxford-uuid:3903e961-25b0-40de-b797-1c455a198d5b |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T20:55:16Z |
| publishDate | 2018 |
| publisher | IEEE |
| record_format | dspace |
| spelling | oxford-uuid:3903e961-25b0-40de-b797-1c455a198d5b2022-03-26T13:53:13ZMulti-task learning using uncertainty to weigh losses for scene geometry and semanticsConference itemhttp://purl.org/coar/resource_type/c_5794uuid:3903e961-25b0-40de-b797-1c455a198d5bEnglishSymplectic ElementsIEEE2018Cipolla, RGal, YKendall, ANumerous 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 considering the homoscedastic uncertainty of each task. This allows us to simultaneously learn various quantities with different units or scales in both classification and regression settings. We demonstrate our model learning per-pixel depth regression, semantic and instance segmentation from a monocular input image. Perhaps surprisingly, we show our model can learn multi-task weightings and outperform separate models trained individually on each task. |
| spellingShingle | Cipolla, R Gal, Y Kendall, A Multi-task learning using uncertainty to weigh losses for scene geometry and semantics |
| title | Multi-task learning using uncertainty to weigh losses for scene geometry and semantics |
| title_full | Multi-task learning using uncertainty to weigh losses for scene geometry and semantics |
| title_fullStr | Multi-task learning using uncertainty to weigh losses for scene geometry and semantics |
| title_full_unstemmed | Multi-task learning using uncertainty to weigh losses for scene geometry and semantics |
| title_short | Multi-task learning using uncertainty to weigh losses for scene geometry and semantics |
| title_sort | multi task learning using uncertainty to weigh losses for scene geometry and semantics |
| work_keys_str_mv | AT cipollar multitasklearningusinguncertaintytoweighlossesforscenegeometryandsemantics AT galy multitasklearningusinguncertaintytoweighlossesforscenegeometryandsemantics AT kendalla multitasklearningusinguncertaintytoweighlossesforscenegeometryandsemantics |