Imperial College London
Alternate

ProfessorAlexandraPorter

Faculty of Engineering, Department of Materials

Professor of Bio-imaging and Analysis
 
 
 
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Contact

 

+44 (0)20 7594 9691a.porter

 
 
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Location

 

B341 Royal School of MinesRoyal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Phillips:2022:10.3390/app12189209,
author = {Phillips, T and Heaney, CE and Benmoufok, E and Li, Q and Hua, L and Porter, AE and Chung, KF and Pain, CC},
doi = {10.3390/app12189209},
journal = {Applied Sciences},
pages = {1--24},
title = {Multi-Output Regression with Generative Adversarial Networks (MOR-GANs)},
url = {http://dx.doi.org/10.3390/app12189209},
volume = {12},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Regression modelling has always been a key process in unlocking the relationships betweenindependent and dependent variables that are held within data. In recent years, machine learninghas uncovered new insights in many fields, providing predictions to previously unsolved problems.Generative Adversarial Networks (GANs) have been widely applied to image processing producinggood results, however, these methods have not often been applied to non-image data. Seeing thepowerful generative capabilities of the GANs, we explore their use, here, as a regression method. Inparticular, we explore the use of the Wasserstein GAN (WGAN) as a multi-output regression method.The resulting method we call Multi-Output Regression GANs (MOR-GANs) and its performanceis compared to a Gaussian Process Regression method (GPR) - a commonly used non-parametricregression method that has been well tested on small datasets with noisy responses. The WGANregression model performs well for all types of datasets and exhibits substantial improvements overthe performance of the GPR for certain types of datasets, demonstrating the flexibility of the GAN asa model for regression.
AU  - Phillips,T
AU  - Heaney,CE
AU  - Benmoufok,E
AU  - Li,Q
AU  - Hua,L
AU  - Porter,AE
AU  - Chung,KF
AU  - Pain,CC
DO  - 10.3390/app12189209
EP  - 24
PY  - 2022///
SN  - 2076-3417
SP  - 1
TI  - Multi-Output Regression with Generative Adversarial Networks (MOR-GANs)
T2  - Applied Sciences
UR  - http://dx.doi.org/10.3390/app12189209
UR  - https://www.mdpi.com/2076-3417/12/18/9209
UR  - http://hdl.handle.net/10044/1/99592
VL  - 12
ER  -
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