Autor(es): Magalhães, V. ; Costa, M. Fernanda P. ; Ferreira, Manuel João Oliveira ; Pinto, T. ; Figueiredo, V.
Data: 2023
Identificador Persistente: https://hdl.handle.net/1822/87618
Origem: RepositóriUM - Universidade do Minho
Assunto(s): Computer vision; Deep learning; Self-supervised learning; SwAV; Industrial spaces
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.
