Stats-aware-GAN: Domain specific statistics matching in generative adversarial networks

State-of-the-art GANs such as StyleGAN2 have been regarded as being capable of mimicking a real data distribution and are able to produce photorealistic images when trained using large datasets of images such as faces of people, animals or natural scenes. However, in the case of images that are specific to a particular engineering domain like radiology or material science, StyleGAN2-generated images, though still seemingly realistic to the untrained eye, have been shown not to follow known distributions of key statistics specific to the domain. This non-adherence of important statistical distributions renders the generated images unsuitable for downstream tasks in that domain. Our work aims to find a way to bridge this gap, i.e., aid GANs (particularly StyleGAN2) in correctly modeling domain specific statistics in images. We study the use of various regularizing loss functions formulated with the goal of either improving distributional similarity or fooling a two sample test. Particularly, we explore using the Bhattacharyya distance and Kullback-Leibler divergence for measuring distributional similarity and smoothened formulations of the Friedman-Rafsky and k-Nearest Neighbor two sample tests, and report our findings.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Industrial Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2024

Author dc:creator

• Kamath, Nidhish Ganesh

Contributors dc:contributor

• Lazebnik, Svetlana

• Generative Adversarial Networks
• Distribution Matching In Gans
• Domain Specific Statistics In Generative Modeling
• Statistical Distributions In Generated Radiology Images
• Gans
• Generative Image Modeling
• Computer Vision For Medical Images
• Medical Image Generation

Statement dc:rights

• Copyright 2024 Nidhish Ganesh Kamath

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/124435

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F124435