Date of Graduation
5-2018
Document Type
Thesis
Degree Name
Master of Science in Computer Science (MS)
Degree Level
Graduate
Department
Computer Science & Computer Engineering
Advisor/Mentor
Michael Gashler
Committee Member
Xintao Wu
Second Committee Member
John Gauch
Keywords
Continuous Functions, Deep Learning, Generative Models, Machine Learning, Neural Networks
Abstract
Generative models are a class of machine learning models capable of producing digital images with plausibly realistic properties. They are useful in such applications as visualizing designs, rendering game scenes, and improving images at higher magnifications. Unfortunately, existing generative models generate only images with a discrete predetermined resolution. This paper presents the Continuous Space Generative Model (CSGM), a novel generative model capable of generating images as a continuous function, rather than as a discrete set of pixel values. Like generative adversarial networks, CSGM trains by alternating between generative and discriminative steps. But unlike generative adversarial networks, CSGM uses only one model for both steps, such that learning can transfer between both operations. Also, the continuous images that CSGM generates may be sampled at arbitrary resolutions, opening the way for new possibilities with generative models. This paper presents results obtained by training on the MNIST dataset of handwritten digits to validate the method, and it elaborates on the potential applications for continuous generative models.
Citation
Komoni, E. (2018). A Continuous Space Generative Model. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/2668