| Texto completo | |
| Autor(es): |
Martinazzo, Ana
;
Espadoto, Mateus
;
Hirata, Nina S. T.
;
IEEE COMP SOC
Número total de Autores: 4
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR); v. N/A, p. 7-pg., 2021-01-01. |
| Resumo | |
In Astronomy, a huge amount of image data is generated daily by photometric surveys, which scan the sky to collect data from stars, galaxies and other celestial objects. In this paper, we propose a technique to leverage unlabeled astronomical images to pre-train deep convolutional neural networks, in order to learn a domain-specific feature extractor which improves the results of machine learning techniques in setups with small amounts of labeled data available. We show that our technique produces results which are in many cases better than using ImageNet pre-training. (AU) | |
| Processo FAPESP: | 17/25835-9 - Interpretação de imagens e de modelos de aprendizado profundos |
| Beneficiário: | Nina Sumiko Tomita Hirata |
| Modalidade de apoio: | Auxílio à Pesquisa - Parceria para Inovação Tecnológica - PITE |
| Processo FAPESP: | 18/25671-9 - Processamento e análise de imagens astronômicas usando modelos convolucionais profundos |
| Beneficiário: | Ana Carolina Rodrigues Cavalcante Martinazzo |
| Modalidade de apoio: | Bolsas no Brasil - Mestrado |