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dc.contributor.advisorRadeva, Petia-
dc.contributor.advisorRódenas Cumplido, Javier-
dc.contributor.authorBergadà Salsen, Joan-
dc.descriptionTreballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2020, Director: Petia Radeva i Javier Ródenas Cumplidoca
dc.description.abstract[en] Convolutional Neural Networks (CNNs) are Deep Learning algorithms that can be applied to a wide range of environments with a high performance. We will study all the elements that form the CNNs, learn how they work, how to train them and how to diagnose its performance. We will also work the state-of-the-art visual explainability algorithms developing how they create their heatmaps. In the practical part we will use CNNs in order to classify food images into their classes and we will apply the studied explainability algorithms to understand the predictions made by the neural networks. Finally, we will perform a both qualitative and quantitative comparison between the explanations given by the applied
dc.format.extent80 p.-
dc.rightsmemòria: cc-nc-nd (c) Joan Bergadà Salsen, 2020-
dc.subject.classificationXarxes neuronals convolucionalsca
dc.subject.classificationReconeixement òptic de formesca
dc.subject.classificationTreballs de fi de grauca
dc.subject.classificationVisió per ordinadorca
dc.subject.classificationAprenentatge automàticca
dc.subject.otherConvolutional neural networksen
dc.subject.otherOptical pattern recognitionen
dc.subject.otherComputer softwareen
dc.subject.otherComputer visionen
dc.subject.otherMachine learningen
dc.subject.otherBachelor's thesesen
dc.titleAnalyzing state-of-the-art CNN’s explainability focusing on food classificationca
Appears in Collections:Treballs Finals de Grau (TFG) - Enginyeria Informàtica
Treballs Finals de Grau (TFG) - Matemàtiques

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