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Detection of misclassified and adversarial examples in Deep Learning
Citation
Marco Blanco, Jorge (2019). Detection of misclassified and adversarial examples in Deep Learning. Thesis (Master thesis), E.T.S. de Ingenieros Informáticos (UPM).
Description
| Title: | Detection of misclassified and adversarial examples in Deep Learning |
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| Item Type: | Thesis (Master thesis) |
| Masters title: | Inteligencia Artificial |
| Date: | 2019 |
| Subjects: | |
| Faculty: | E.T.S. de Ingenieros Informáticos (UPM) |
| Department: | Inteligencia Artificial |
| Creative Commons Licenses: | Recognition - No derivative works - Non commercial |
Abstract
Great developments have been carried out thanks to deep learning in recent years. However, for a massive adoption in real-world applications, deep learning models need to be trustworthy. In this work we identify scenarios where such models behave in an unexpected fashion, implement a method based on the entropy for assessing their reliability and build a deep learning-based system able to detect its own wrong predictions. Additionally, by using the mentioned method, we achieve a relevant improvement of accuracy with an ensemble of learners.
More information
| Item ID: | 56004 |
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| DC Identifier: | https://oa.upm.es/56004/ |
| OAI Identifier: | oai:oa.upm.es:56004 |
| Deposited by: | Biblioteca Facultad de Informatica |
| Deposited on: | 09 Aug 2019 06:38 |
| Last Modified: | 09 Aug 2019 06:38 |
