Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/125161
Title: Attribution methods for deep convolutional networks
Author: Brasó Andilla, Guillem
Director/Tutor: Vitrià i Marca, Jordi
Keywords: Xarxes neuronals (Informàtica)
Treballs de fi de grau
Algorismes computacionals
Visió per ordinador
Neural networks (Computer science)
Bachelor's thesis
Computer algorithms
Computer vision
Issue Date: 27-Jun-2018
Abstract: [en] In recent years, Deep Learning has shown great success across several areas. However, even, though it might provide remarkable accuracy for many tasks, its application in some fields faces a fundamental problem: its predictions are not interpretable. Attribution Methods offer a possible solution in regards to this problem. To do so, they resource to results in Game Theory in order to explain individual decisions made by Deep Learning algorithms. In this work, we will be focusing, specifically, on the application of Attribution Techniques to a subset of Deep Learning algorithms: Convolutional Neural Networks.
Note: Treballs Finals de Grau de Matemàtiques, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2018, Director: Jordi Vitrià i Marca
URI: http://hdl.handle.net/2445/125161
Appears in Collections:Treballs Finals de Grau (TFG) - Matemàtiques

Files in This Item:
File Description SizeFormat 
memoria.pdfMemòria1.7 MBAdobe PDFView/Open


This item is licensed under a Creative Commons License Creative Commons