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Generative neural networks for sky image outpainting
 Mrázek, Matěj ; Šikudová, Elena (advisor) ; Mirbauer, Martin (referee)
Image outpainting is a task in the area of generative artificial intelligence, where the goal is to expand an image in a feasible way. The goal of this work is to create a machine learning algorithm capable of sky image outpainting by implementing sev- eral recently proposed techniques in the field. We train three models, a tokenizer for converting images to tokens and back, a masked generative transformer for performing outpainting on tokens and a super sampler for upscaling the result, all on a dataset of sky images. Then, we propose a procedure that combines the trained models to solve the outpainting task. We describe the results of training each model and those of the fi- nal algorithm. Our contribution consists mainly in providing a working, open-source implementation including the trained models capable of sky image outpainting. 1
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