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dc.contributorSanfeliu Cortés, Alberto
dc.contributor.authorSalvia Punsoda, Víctor
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.description.abstractIn this work we will try to break down the fundamentals of deep generative models for image generation. For the sake of simplicity and understanding we will be using the MNIST dataset, which can be easily trained and understood. The algorithms that we will be explaining and comparing are an autoregressive model, a flow-based model, a variational autoencoder, and a generative adversarial network. Within each of these categories, we have chosen simple algorithms for the sake of clarity as well. Keep in mind that this is a wide but introductory work to the area of image generation. However, these algorithms were certainly state-of the-art around 2016.
dc.publisherUniversitat Politècnica de Catalunya
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subject.lcshArtificial intelligence
dc.subject.otherMachine Learning
dc.subject.otherDeep Learning
dc.subject.otherArtificial Neural Networks
dc.subject.otherDeep Generative models
dc.subject.otherImage Generation
dc.subject.otherComputer Vision
dc.subject.otherAutoregressive Models
dc.subject.otherFlow-Based Models.
dc.titleAnalysis of state-of-the-art deep generative models for images
dc.typeBachelor thesis
dc.subject.lemacIntel·ligència artificial
dc.subject.amsClassificació AMS::68 Computer science::68T Artificial intelligence
dc.rights.accessOpen Access
dc.audience.mediatorUniversitat Politècnica de Catalunya. Facultat de Matemàtiques i Estadística
dc.audience.degreeGRAU EN MATEMÀTIQUES (Pla 2009)

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