Versatile Auxiliary Classification and Regression With Generative Adversarial Networks

One of the most interesting challenges in Artificial Intelligence is to train conditional generators which are able to provide labeled adversarial samples drawn from a specific distribution. For a successful implementation of conditional generators, the created samples are constrained to a specific...

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Main Authors:	Shabab Bazrafkan, Viktor Varkarakis, Joseph Lemley, Hossein Javidnia, Peter Corcoran
Format:	Article
Language:	English
Published:	IEEE 2021-01-01
Series:	IEEE Access
Subjects:	Conditional generators deep neural networks generative adversarial networks
Online Access:	https://ieeexplore.ieee.org/document/9369348/

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spelling	doaj-c7c57e7519be44a0a116e22ec93925082021-03-30T15:21:21ZengIEEEIEEE Access2169-35362021-01-019388103882510.1109/ACCESS.2021.30637939369348Versatile Auxiliary Classification and Regression With Generative Adversarial NetworksShabab Bazrafkan0Viktor Varkarakis1https://orcid.org/0000-0002-3877-2802Joseph Lemley2https://orcid.org/0000-0002-0595-2313Hossein Javidnia3Peter Corcoran4https://orcid.org/0000-0003-1670-4793Department of Physics, University of Antwerp, Antwerp, BelgiumCollege of Engineering and Informatics, National University of Ireland Galway, Galway, IrelandCollege of Engineering and Informatics, National University of Ireland Galway, Galway, IrelandSchool of Computer Science and Statistics ADAPT Centre, Trinity College Dublin, Dublin 2, IrelandCollege of Engineering and Informatics, National University of Ireland Galway, Galway, IrelandOne of the most interesting challenges in Artificial Intelligence is to train conditional generators which are able to provide labeled adversarial samples drawn from a specific distribution. For a successful implementation of conditional generators, the created samples are constrained to a specific class. In this work, a new framework is presented to train a deep conditional generator by placing a classifier or regression model in parallel with the discriminator and back propagate the classification or regression error through the generator network. Special cases for binary classification, multi-class classification, and regression are studied. Experimental results on several data-sets are provided and the results are compared with similar state-of-the-art techniques. The main advantage of the method is that it is versatile and applicable to any variation of Generative Adversarial Network (GAN) implementation but also it is shown to obtain superior results compared to other methods. The mathematical proofs for the proposed scheme for both classification and regression are presented.https://ieeexplore.ieee.org/document/9369348/Conditional generatorsdeep neural networksgenerative adversarial networks
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Shabab Bazrafkan Viktor Varkarakis Joseph Lemley Hossein Javidnia Peter Corcoran
spellingShingle	Shabab Bazrafkan Viktor Varkarakis Joseph Lemley Hossein Javidnia Peter Corcoran Versatile Auxiliary Classification and Regression With Generative Adversarial Networks IEEE Access Conditional generators deep neural networks generative adversarial networks
author_facet	Shabab Bazrafkan Viktor Varkarakis Joseph Lemley Hossein Javidnia Peter Corcoran
author_sort	Shabab Bazrafkan
title	Versatile Auxiliary Classification and Regression With Generative Adversarial Networks
title_short	Versatile Auxiliary Classification and Regression With Generative Adversarial Networks
title_full	Versatile Auxiliary Classification and Regression With Generative Adversarial Networks
title_fullStr	Versatile Auxiliary Classification and Regression With Generative Adversarial Networks
title_full_unstemmed	Versatile Auxiliary Classification and Regression With Generative Adversarial Networks
title_sort	versatile auxiliary classification and regression with generative adversarial networks
publisher	IEEE
series	IEEE Access
issn	2169-3536
publishDate	2021-01-01
description	One of the most interesting challenges in Artificial Intelligence is to train conditional generators which are able to provide labeled adversarial samples drawn from a specific distribution. For a successful implementation of conditional generators, the created samples are constrained to a specific class. In this work, a new framework is presented to train a deep conditional generator by placing a classifier or regression model in parallel with the discriminator and back propagate the classification or regression error through the generator network. Special cases for binary classification, multi-class classification, and regression are studied. Experimental results on several data-sets are provided and the results are compared with similar state-of-the-art techniques. The main advantage of the method is that it is versatile and applicable to any variation of Generative Adversarial Network (GAN) implementation but also it is shown to obtain superior results compared to other methods. The mathematical proofs for the proposed scheme for both classification and regression are presented.
topic	Conditional generators deep neural networks generative adversarial networks
url	https://ieeexplore.ieee.org/document/9369348/
work_keys_str_mv	AT shababbazrafkan versatileauxiliaryclassificationandregressionwithgenerativeadversarialnetworks AT viktorvarkarakis versatileauxiliaryclassificationandregressionwithgenerativeadversarialnetworks AT josephlemley versatileauxiliaryclassificationandregressionwithgenerativeadversarialnetworks AT hosseinjavidnia versatileauxiliaryclassificationandregressionwithgenerativeadversarialnetworks AT petercorcoran versatileauxiliaryclassificationandregressionwithgenerativeadversarialnetworks
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Versatile Auxiliary Classification and Regression With Generative Adversarial Networks

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