Implementació d'un procés de transferència d'estil mitjançant una GAN
This work is based on the application of generative adversarial networks (GAN) to transfer the style of a set of images, specific to an author, to an input image. Specifically, we want to achieve models capable of generating new images, given an input real photograph as input, applying the transfer...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universitat Oberta de Catalunya (UOC) |
| Repositorio: | O2, repositorio institucional de la UOC |
| OAI Identifier: | oai:openaccess.uoc.edu:10609/123406 |
| Acceso en línea: | http://hdl.handle.net/10609/123406 |
| Access Level: | acceso abierto |
| Palabra clave: | generative adversarial network style transfer deep learning xarxes generatives adversarials transferència d'estil aprenentatge profund redes generativas adversariales transferencia de estilo aprendizaje profundo Data mining -- TFM Mineria de dades -- TFM Minería de datos -- TFM |
| Sumario: | This work is based on the application of generative adversarial networks (GAN) to transfer the style of a set of images, specific to an author, to an input image. Specifically, we want to achieve models capable of generating new images, given an input real photograph as input, applying the transfer of style of paintings by van Gogh, Picasso and Pollock. This study delves into the different characteristics of the images processed by the networks and the components involved in the style transfer process. It is based on the configuration and treatment of losses described in the article "Artsy" GAN: A style transfer system with improved quality, diversity, and performance¿ by Liu et al. (2016). This paper proposes an adversarial generative approach using perceptual loss, processing images with chroma subsampling, introducing noise into generator input images, and a loss target function that encourages generating different details for the same content image. These modifications are intended to improve the performance and quality of the results obtained with previous studies, such as the use of CycleGan's. |
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