Conditioning diffusion-based generative models with semantical binary attribute descriptors
In this thesis, we present a novel architecture that enables image generation by dataset-specific binary attributes. The resulting pipeline has been part of a larger research project done between Northeastern University and UPenn. This thesis describe the main contributions to it, and the rationale...
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| Formato: | tesis de maestría |
| Fecha de publicación: | 2025 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/452213 |
| Acesso em linha: | https://hdl.handle.net/2117/452213 |
| Access Level: | acceso abierto |
| Palavra-chave: | Computer vision Deep learning Diffusion models Encodings Stable Diffusion Binary attributes Visió per ordinador Aprenentatge profund Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
| Resumo: | In this thesis, we present a novel architecture that enables image generation by dataset-specific binary attributes. The resulting pipeline has been part of a larger research project done between Northeastern University and UPenn. This thesis describe the main contributions to it, and the rationale behind its design pieces. |
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