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dc.contributor.advisorRadeva, Petia-
dc.contributor.advisorRódenas Cumplido, Javier-
dc.contributor.authorMladenova, Mariya-
dc.contributor.authorTonchev, Petar-
dc.descriptionTreballs finals del Màster de Fonaments de Ciència de Dades, Facultat de matemàtiques, Universitat de Barcelona, Any: 2020, Tutor: Petia Radeva i Javier Ródenas Cumplidoca
dc.description.abstract[en] Nowadays, with the help of the novel machine learning models, in particular Generative Adversarial Networks, we are able to generate synthetic media, which look absolutely realistic and at the same time authentic. Still, the food image-to-image translation remains a challenging problem that is very unexplored. Due to the complexity of food images the state of the art results are noisy and slow in convergence. In our work, we explore how adding attention to the image-to-image translation on food data can produce more realistic synthetic images and speed up the convergence of the algorithm. Furthermore, we present extensive analysis of GANs for food image synthesis and discuss several possible improvements over the base methodology sharing our insights on this problem. The source code that has been used to produce the results in this project can be found in our GitLab repository:
dc.format.extent58 p.-
dc.rightscc-by-nc-nd (c) Mariya Mladenova i Petar Tonchev, 2020-
dc.subject.classificationIntel·ligència artificial-
dc.subject.classificationXarxes neuronals (Informàtica)-
dc.subject.classificationTreballs de fi de màster-
dc.subject.classificationAprenentatge automàtic-
dc.subject.otherArtificial intelligence-
dc.subject.otherNeural networks (Computer science)-
dc.subject.otherMaster's theses-
dc.subject.otherMachine learning-
dc.titleAttention Guided Image-to-Image translation of Food Images Using GANsca
Appears in Collections:Programari - Treballs de l'alumnat
Màster Oficial - Fonaments de la Ciència de Dades

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