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