Imperial College London
Alternate

ProfessorAndrewDavison

Faculty of Engineering, Department of Computing

Professor of Robot Vision
 
 
 
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Contact

 

+44 (0)20 7594 8316a.davison Website

 
 
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Assistant

 

Ms Lucy Atthis +44 (0)20 7594 8259

 
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Location

 

303William Penney LaboratorySouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Liu:2019,
author = {Liu, S and Davison, A and Johns, E},
publisher = {Neural Information Processing Systems Foundation, Inc.},
title = {Self-supervised generalisation with meta auxiliary learning},
url = {http://hdl.handle.net/10044/1/77546},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Learning with auxiliary tasks can improve the ability of a primary task to generalise.However, this comes at the cost of manually labelling auxiliary data. We propose anew method which automatically learns appropriate labels for an auxiliary task,such that any supervised learning task can be improved without requiring access toany further data. The approach is to train two neural networks: a label-generationnetwork to predict the auxiliary labels, and a multi-task network to train theprimary task alongside the auxiliary task. The loss for the label-generation networkincorporates the loss of the multi-task network, and so this interaction between thetwo networks can be seen as a form of meta learning with a double gradient. Weshow that our proposed method, Meta AuXiliary Learning (MAXL), outperformssingle-task learning on 7 image datasets, without requiring any additional data.We also show that MAXL outperforms several other baselines for generatingauxiliary labels, and is even competitive when compared with human-definedauxiliary labels. The self-supervised nature of our method leads to a promisingnew direction towards automated generalisation. Source code can be found athttps://github.com/lorenmt/maxl.
AU  - Liu,S
AU  - Davison,A
AU  - Johns,E
PB  - Neural Information Processing Systems Foundation, Inc.
PY  - 2019///
TI  - Self-supervised generalisation with meta auxiliary learning
UR  - http://hdl.handle.net/10044/1/77546
ER  -
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