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Explainable AI

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.

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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