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Multimodal-source image generation with deep learning

Fabrizio Lande

Multimodal-source image generation with deep learning.

Rel. Paolo Garza, Erfan Ghaderey, Ruben Cartuyvels Cartuyvels. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021

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Abstract:

The focus of this work almost completely carried on during my stay at KU Leuven University as part of my Erasmus project, is to present a new way of synthesizing images starting from a descriptive input text and a reference image by using a model, RaGAN, extensively based on deep learning and generative adversarial network, that could set a base for future experimentation in this hybrid field that is multimodal source image generation.

Relators: Paolo Garza, Erfan Ghaderey, Ruben Cartuyvels Cartuyvels
Academic year: 2021/22
Publication type: Electronic
Number of Pages: 80
Subjects:
Corso di laurea: Corso di laurea magistrale in Data Science And Engineering
Classe di laurea: New organization > Master science > LM-32 - COMPUTER SYSTEMS ENGINEERING
Ente in cotutela: KUL - KATHOLIEKE UNIVERSITEIT LEUVEN (BELGIO)
Aziende collaboratrici: Ku Leuven
URI: http://webthesis.biblio.polito.it/id/eprint/21219
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