| Texto completo | |
| Autor(es): |
Cavallari, Gabriel B.
;
Ponti, Moacir A.
;
IEEE Comp Soc
Número total de Autores: 3
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2021 34TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI 2021); v. N/A, p. 8-pg., 2021-01-01. |
| Resumo | |
Self-supervised learning approaches were shown to benefit feature learning by training models under a pretext task. In this context, learning from limited data can be tackled using a combination of semi-supervised learning and self-supervision. In this paper we combine the traditional supervised learning paradigm with the rotation prediction self-supervised task, that are used simultaneously to train a siamese model with a joint loss function and shared weights. In particular, we are interested in the case in which the proportion of labeled with respect to unlabeled data is small. We investigate the effectiveness of a compact feature space obtained after training under such limited annotation scenario, in terms of linear class separability and under attack. The study includes images from multiple domains, such as natural images (STL-10 dataset), products (Fashion-MNIST dataset) and biomedical images (Malaria dataset). We show that in scenarios where we have only a few labeled data the model augmented with a self-supervised task can take advantage of the unlabeled data to improve the learned representation in terms of the linear discrimination, as well as allowing learning even under attack. Also, we discuss the choices in terms of self-supervision and cases of failure considering the different datasets. (AU) | |
| Processo FAPESP: | 19/07316-0 - Teoria de singularidades e aplicações a geometria diferencial, equações diferenciais e visão computacional |
| Beneficiário: | Farid Tari |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |
| Processo FAPESP: | 19/02033-0 - Estudo de representações de imagens de múltiplos domínios a partir de aprendizado profundo não supervisionado e semi-supervisionado |
| Beneficiário: | Gabriel Biscaro Cavallari |
| Modalidade de apoio: | Bolsas no Brasil - Mestrado |