
Taha Zafar
Explainable AI.
Rel. Giovanni Squillero, Alberto Paolo Tonda, Pietro Barbiero. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2022
Abstract: |
Unsupervised conceptual extraction in Deep Neural Networks. Stemming from the recent advances in Explainable AI (XAI), the work will train deep neural networks to classify well-known paintings according to standard artistic taxonomy (genre, style, ...) to ultimately generate and study the "artificial concepts" that led to a decision. Such "artificial concepts", representing high-level entities such as use of color and strokes, will be autonomously uncovered by the network, thanks to Logic Layers, and later analyzed by an expert. |
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Relators: | Giovanni Squillero, Alberto Paolo Tonda, Pietro Barbiero |
Academic year: | 2021/22 |
Publication type: | Electronic |
Number of Pages: | 80 |
Additional Information: | Tesi secretata. Fulltext non presente |
Subjects: | |
Corso di laurea: | Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering) |
Classe di laurea: | New organization > Master science > LM-32 - COMPUTER SYSTEMS ENGINEERING |
Ente in cotutela: | Pontifícia Universidade Católica do Rio de Janeiro (BRASILE) |
Aziende collaboratrici: | UNSPECIFIED |
URI: | http://webthesis.biblio.polito.it/id/eprint/22688 |
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