Dissertation/Thesis Abstract

Large Scale Semantic Image Inpainting with Progressively Grown GANs
by Lin, Kelvin, M.E., The Cooper Union for the Advancement of Science and Art, 2020, 87; 27996185
Abstract (Summary)

This thesis studies the efficacy of using progressively grown GANs for use in image inpainting through constrained image generation. This method uses the pixels in a target image to constrain a GAN. An ℓ1 error function is constructed using these constraints, and input back-propagation is used to traverse the error manifold. A result set of inputs can be calculated in the latent space of the GAN in order to produce an image with high resemblance to the target image. It is shown that large network sizes can be beneficial to the effectiveness of inpainting with constrained image generation.

Indexing (document details)
Advisor: Keene, Sam
School: The Cooper Union for the Advancement of Science and Art
Department: Electrical Engineering
School Location: United States -- New York
Source: MAI 82/3(E), Masters Abstracts International
Subjects: Electrical engineering, Engineering
Keywords: GAN, Semantic image inpainting
Publication Number: 27996185
ISBN: 9798672184159
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