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https://hdl.handle.net/2445/183203
Title: | Analyzing state-of-the-art CNN’s explainability focusing on food classification |
Author: | Bergadà Salsen, Joan |
Director/Tutor: | Radeva, Petia Ródenas Cumplido, Javier |
Keywords: | Xarxes neuronals convolucionals Reconeixement òptic de formes Programari Treballs de fi de grau Visió per ordinador Aprenentatge automàtic Aliments Convolutional neural networks Optical pattern recognition Computer software Computer vision Machine learning Bachelor's theses Food |
Issue Date: | 21-Jun-2020 |
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 algorithms. |
Note: | Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2020, Director: Petia Radeva i Javier Ródenas Cumplido |
URI: | https://hdl.handle.net/2445/183203 |
Appears in Collections: | Treballs Finals de Grau (TFG) - Enginyeria Informàtica Treballs Finals de Grau (TFG) - Matemàtiques |
Files in This Item:
File | Description | Size | Format | |
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tfg_joan_bergada_salsen.pdf | Memòria | 63.49 MB | Adobe PDF | View/Open |
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