| 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 |
|