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Title:Training large-scale video generative adversarial networks for high quality video synthesis
Author(s):Du, Xiaodan
Advisor(s):Lazebnik, Svetlana
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Generative Adversarial Networks
video synthesis
computer vision
Abstract:Video synthesis using deep learning methods is an important yet challenging task for the computer vision community. Generative Adversarial Networks have been proved effective for generating high fidelity photo-realistic images. Recently, many video synthesis models achieve high fidelity and resolution samples by carrying the success of Generative Adversarial Networks to the field of video synthesis. However, it can be challenging to train large-scale Generative Adversarial Networks as they often require enormous computing resources and a long training period. We found it necessary to put together a clear and in-depth guideline for researchers who are interested in training large-scale video Generative Adversarial Networks in the future. In this thesis, we aim to find effective and efficient ways to implement and train large-scale video Generative Adversarial Networks for high quality video generation. We evaluate different implement choices as well as training details and give quantitative analysis.
Issue Date:2020-05-13
Rights Information:Copyright 2020 Xiaodan Du
Date Available in IDEALS:2020-08-26
Date Deposited:2020-05

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