methodology-9
Auxiliary Learning
3260 papers • 126 benchmarks • 313 datasets
Auxiliary learning aims to find or design auxiliary tasks which can improve the performance on one or some primary tasks. ( Image credit: Self-Supervised Generalisation with Meta Auxiliary Learning )
(Image credit: Papersgraph)
Benchmarks
These leaderboards are used to track progress in auxiliary-learning-19
Trend
Dataset
Best Model
Actions
No benchmarks available.
Libraries
iUse these libraries to find auxiliary-learning-19 models and implementations
Datasets
No datasets available.
Subtasks
No subtasks available.
Most implemented papers
Self-Supervised Generalisation with Meta Auxiliary Learning
A. Davison, Shikun Liu, Edward Johns•Thu Jan 24 2019
The proposed method, Meta AuXiliary Learning (MAXL), outperforms single-task learning on 7 image datasets, without requiring any additional data, and is even competitive when compared with human-defined auxiliary labels.
173