Pietro Recalcati
Enhancing deep learning techniques for computational social media image analysis with out-of-distribution detection.
Rel. Fabrizio Lamberti, Lia Morra. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2022
Abstract
Social networks are one of the most interesting and widespread phenomenons characterizing our time. Consequently, the involvement and attitude people show towards them are particularly relevant for researchers in several fields, including psychology, philosophy, semiotics and social studies. The ever-increasing volume of interactions carried out by users on this kind of platforms, however, prevents experts from being able to manually inspect a sufficient amount of data in order to grasp a complete picture, motivating the need for the automation of information extraction. This thesis describes the infrastructure of a system able to collect and process data coming from social networks, producing quantitative outputs which can be exploited by semioticians in the context of the research project FACETS (Face Aesthetics in Contemporary E-Technological Societies).
The input data, consisting mostly of profile pictures, are treated by means of several Computer Vision algorithms capable of extracting a specific kind of information
Relatori
Anno Accademico
Tipo di pubblicazione
Numero di pagine
Informazioni aggiuntive
Corso di laurea
Classe di laurea
Aziende collaboratrici
URI
![]() |
Modifica (riservato agli operatori) |
