Real image transformation through conditional GANs
Generative Adversarial Networks (GANs) has become a new big topic as they are able to produce diverse high-resolution and photo-realistic images, often indistinguishable from real images and this ability has powered many real-world applications. However, these networks are limited on generating from...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/343954 |
| Acceso en línea: | https://hdl.handle.net/2117/343954 |
| Access Level: | acceso embargado |
| Palabra clave: | deep learning Computer Vision GANs decoration |
| Sumario: | Generative Adversarial Networks (GANs) has become a new big topic as they are able to produce diverse high-resolution and photo-realistic images, often indistinguishable from real images and this ability has powered many real-world applications. However, these networks are limited on generating from random samples (latent vectors) with the image domain trained for. The interest of this thesis is to be able to adapt this type of models and increase the functionality of them by transforming real images through attribute conditions. |
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