Improved conditional generative adversarial networks for image generation : methods and their application in knowledge distillation
department or school: Science, Faculty of
department or school: Statistics, Department of
data provider: DSpace
degree campus: UBCV
creator: Ding, Xin
issued: 2021-09-02T15:12:30Z
issued: 2021
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: Conditional generative adversarial networks (cGANs) are state-of-the-art models for synthesizing images dependent on some conditions. These conditions are usually categorical variables such as class labels. cGANs with class labels as conditions are also known as class-conditional GANs. Some modern class-conditional GANs such as BigGAN can even generate photo-realistic images. The success of cGANs has been shown in various applications. However, two weaknesses of cGANs still exist. First, image generation conditional on continuous, scalar variables (termed regression labels) has never been studied. Second, low-quality fake images still appear frequently in image synthesis with state-of-the-art cGANs, especially when training data are limited. This thesis aims to resolve the above two weaknesses of cGANs and explore the applications of cGANs in improving a lightweight model with the knowledge from a heavyweight model (i.e., knowledge distillation). First, existing empirical losses and label input mechanisms of cGANs are not suitable for regression labels, making cGANs fail to synthesize images conditional on regression labels. To solve this problem, this thesis proposes the continuous conditional generative adversarial network (CcGAN), including novel empirical losses and label input mechanisms. Moreover, even the state-of-the-art cGANs may produce low-quality images, so a subsampling method to drop these images is necessary. In this thesis, we propose a density ratio based subsampling framework for unconditional GANs. Then, we introduce its extension to the conditional image synthesis setting called cDRE-F-cSP+RS, which can effectively improve the image quality of both class-conditional GANs and CcGAN. Finally, we propose a unified knowledge distillation framework called cGAN-KD suitable for both image classification and regression (with a scalar response), where the synthetic data generated from class-conditional GANs and CcGAN are used to transfer knowledge from a …
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/79599
note: Improved Conditional Generative Adversarial Networksfor Image Generation: Methods and Their Application inKnowledge DistillationbyXin DingB.Sc., Anhui University, 2014M.Sc., University of Illinois at Urbana-Champaign, 2016A THESIS SUBMITTED IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OFDoctor of PhilosophyinTHE FACULTY OF GRADUATE AND POSTDOCTORALSTUDIES(Statistics)The University of British Columbia(Vancouver)August 2021© Xin Ding, 2021The following individuals certify that they have read, and recommend to the Fac-ulty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled:Improved Conditional Generative Adversarial Networks for Image Gen-eration: Methods and Their Application in Knowledge Distillationsubmitted by Xin Ding in partial fulfillment of the requirements for the degree ofDoctor of Philosophy in Statistics.Examining Committee:William J. Welch, Department of StatisticsCo-supervisorZ. Jane Wang, Department of Electrical and Computer EngineeringCo-supervisorPurang Abolmaesumi, Department of Electrical and Computer EngineeringUniversity ExaminerEldad Haber, Department of Earth, Ocean and Atmospheric SciencesUniversity ExaminerAdditional Supervisory Committee Members:Benjamin Bloem-Reddy, Department of StatisticsSupervisory Committee MemberiiAbstractConditional generative adversarial networks (cGANs) are state-of-the-art mod-els for synthesizing images dependent on some conditions. These conditions areusually categorical variables such as class labels. cGANs with class labels as con-ditions are also known as class-conditional GANs. Some modern class-conditionalGANs such as BigGAN can even generate photo-realistic images. The successof cGANs has been shown in various applications. However, two weaknessesof cGANs still exist. First, image generation conditional on continuous, scalarvariables (termed regression labels) has never been studied. Second, low-qualityfake images still appear frequently in image synthesis with state-of-the-…
has type: Thesis/Dissertation
date issued: 2021-11
is shown at: 10.14288/1.0401890
language: eng
degree discipline: Statistics
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Welch, William J.
contributor: Wang, Z. Jane
type: Text
identifier URI: http://hdl.handle.net/2429/79599