Cristian Canton Ferrer

Segueix-nos a xarxes per estar al corrent de les últimes novetats Hi ha un moment en què els governs descobreixen que el sistema educatiu no funciona només amb decrets. Funciona perquè cada matí desenes de milers de docents encenen els llums i entren a les aules. I quan aquest col·lectiu decideix aturar-se, l’educació deixa de ser una abstracció administrativa i es converteix en un servei essencial que cal mantenir en funcionament.

Abstract Artificial Intelligence (AI) has seamlessly integrated into numerous scientific domains, catalysing unparalleled enhancements across a broad spectrum of tasks; however, its integrity and trustworthiness have emerged as notable concerns. The scientific community has focused on the development of trustworthy AI algorithms; however, machine learning and deep learning algorithms, popular in the AI community today, intrinsically rely on the quality of their training data.

This paper introduces a novel approach to in-painting where the identity of the object to remove or change is preserved and accounted for at inference time: Exemplar GANs (ExGANs). ExGANs are a type of conditional GAN that utilize exemplar information to produce high-quality, personalized in-painting results. We propose using exemplar information in the form of a reference image of the region to in-paint, or a perceptual code describing that object.