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Variably trained generative adversarial networks

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TitleInfo
Title
Variably trained generative adversarial networks
Name (type = personal)
NamePart (type = family)
Sridhar
NamePart (type = given)
Vibha
NamePart (type = date)
1996
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Vibha Sridhar
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author
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Lun
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Desmond
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Desmond Lun
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Advisory Committee
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chair
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NamePart (type = family)
Gandhi
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Rajiv
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Rajiv Gandhi
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Advisory Committee
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internal member
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Birget
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Jean-Camille
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Jean-Camille Birget
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Advisory Committee
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internal member
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Rutgers University
Role
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degree grantor
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Camden Graduate School
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school
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theses
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2019
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2019-05
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2019
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English
Abstract
Since their introduction in 2014, Generative Adversarial Networks (GAN), have been a hot topic in the AI field. Although the original implementation of GAN is for the generation of images, researchers have progressed beyond that and have created GAN variants that can generate music, perform style transfer and much more. One pitfall of GANs is that they are challenging to train. Many tricks have since been suggested for improved training. In this thesis, we combine these techniques with a state-of-the-art GAN variant in an attempt to improve GAN performance. We implement the Variably Trained GAN (VT-GAN) that combines the features of an Auxiliary Classifier GAN with a deep convolutional neural network architecture, and Label Smoothing and Minibatch Discrimination layer techniques to improve and stabilize GAN training, to generate realistic high quality images. The evaluation metric used is the Inception Score that gives a quantitative value measuring the realness of an image. Although it takes longer for VT-GAN to train due to the addition of complex mathematical operations, for the same amount of training, the VT-GAN performs approximately 3\% better than the AC-GAN with respect to the Inception Score produced.
Subject (authority = RUETD)
Topic
Computer Science
Subject (authority = ETD-LCSH)
Topic
Artificial intelligence
Subject (authority = ETD-LCSH)
Topic
Machine learning
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Rutgers University Electronic Theses and Dissertations
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ETD_10047
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application/pdf
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text/xml
Note
Supplementary File: Exploiting Vector Arithmetic Properties of Generator
Extent
1 online resource (vi, 40 pages) : illustrations
Note (type = degree)
M.S.
Note (type = bibliography)
Includes bibliographical references
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Camden Graduate School Electronic Theses and Dissertations
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rucore10005600001
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Identifier (type = doi)
doi:10.7282/t3-tasd-fg61
Genre (authority = ExL-Esploro)
ETD graduate
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Rights

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The author owns the copyright to this work.
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Name
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Sridhar
GivenName
Vibha
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RightsEvent
Type
Permission or license
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2019-05-15 12:51:48
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Name
Vibha Sridhar
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Affiliation
Rutgers University. Camden Graduate School
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Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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Type
Embargo
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2019-05-31
DateTime (encoding = w3cdtf); (qualifier = exact); (point = end)
2020-05-30
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after May 30th, 2020.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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