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Attribution methods for deep convolutional networks

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dc.contributor Vitrià i Marca, Jordi
dc.creator Brasó Andilla, Guillem
dc.date 2018-10-09T08:25:58Z
dc.date 2018-10-09T08:25:58Z
dc.date 2018-06-27
dc.date.accessioned 2024-12-16T10:26:49Z
dc.date.available 2024-12-16T10:26:49Z
dc.identifier http://hdl.handle.net/2445/125161
dc.identifier.uri http://fima-docencia.ub.edu:8080/xmlui/handle/123456789/21555
dc.description Treballs Finals de Grau de Matemàtiques, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2018, Director: Jordi Vitrià i Marca
dc.description [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.
dc.format 58 p.
dc.format application/pdf
dc.language eng
dc.rights cc-by-nc-nd (c) Guillem Brasó Andilla, 2018
dc.rights http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.rights info:eu-repo/semantics/openAccess
dc.source Treballs Finals de Grau (TFG) - Matemàtiques
dc.subject Xarxes neuronals (Informàtica)
dc.subject Algorismes computacionals
dc.subject Visió per ordinador
dc.subject Treballs de fi de grau
dc.subject Neural networks (Computer science)
dc.subject Computer algorithms
dc.subject Computer vision
dc.subject Bachelor's theses
dc.title Attribution methods for deep convolutional networks
dc.type info:eu-repo/semantics/bachelorThesis


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