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BayesBiGAN and GANify: Research and ...

Dinjian, Daniel.

BayesBiGAN and GANify: Research and a Developer Tool for Generative Adversarial Networks.

Record Type:	Electronic resources : Monograph/item
Title/Author:	BayesBiGAN and GANify: Research and a Developer Tool for Generative Adversarial Networks.
Author:	Dinjian, Daniel.
Published:	Ann Arbor : ProQuest Dissertations & Theses, 2019
Description:	35 p.
Notes:	Source: Masters Abstracts International, Volume: 81-04.
Notes:	Advisor: Liu, Liping.
Contained By:	Masters Abstracts International81-04.
Subject:	Computer science.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=22584752
ISBN:	9781392752692

BayesBiGAN and GANify: Research and a Developer Tool for Generative Adversarial Networks.
Dinjian, Daniel.

BayesBiGAN and GANify: Research and a Developer Tool for Generative Adversarial Networks. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 35 p.

Source: Masters Abstracts International, Volume: 81-04.

Thesis (M.S.)--Tufts University, 2019.

This item must not be sold to any third party vendors.

This thesis concerns the current state of Generative Adversarial Networks (GANs) in the field of Machine Learning. The first part of the work combines the BayesGAN and the BiGAN models to create the Bayesian Bidirectional GAN (BayesBiGAN). The BayesBiGAN uses the BayesGAN’s MCMC training method to sample from posteriors over the generator, discriminator, and the BiGAN encoder. This model captures a rich underlying feature representation and encodes signals to a lower dimensional vectors. The second part of the work addresses the disconnect between GAN research and product development. GAN research is far less accessible than research in other machine learning fields due to a lack of developer tools. This drastically limits the speed and breadth of applications that can be made. This work begins to address the disconnect by creating an API that offers cloud access to CycleGAN for style transfer to convert photos into painting renderings in the style of various famous painters. The API allows developers to easily integrate the CycleGAN into their own work. An educational demo website demonstrates how the API can be used to engage a non-technical audience.

ISBN: 9781392752692Subjects--Topical Terms:

199325
Computer science.

BayesBiGAN and GANify: Research and a Developer Tool for Generative Adversarial Networks.
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