Publicação

Application of a self-supervised learning technique for monitoring industrial spaces

Ver documento

Detalhes bibliográficos
Resumo:Supervised learning has reached a bottleneck as they require expensive and time-consuming annotations. In addition, in some problems, such as in industrial spaces, it is not always possible to acquire a large number of images. Self-supervised learning helps these issues by extracting information from the data itself, without requiring labels and has achieved good performance, closing the gap between supervised and self-supervised learning. This work presents the application of a self-supervised learning method - SwAV, that classifies anomalies in an industrial space, evaluates its performance and compares the results to the supervised paradigm.
Autores principais:Magalhães, V.
Outros Autores:Costa, M. Fernanda P.; Ferreira, Manuel João Oliveira; Pinto, T.; Figueiredo, V.
Assunto:Computer vision Deep learning Self-supervised learning SwAV Industrial spaces
Ano:2023
País:Portugal
Tipo de documento:comunicação em conferência
Tipo de acesso:acesso aberto
Instituição associada:Universidade do Minho
Idioma:inglês
Origem:RepositóriUM - Universidade do Minho

Registos relacionados

Registos relacionados