| Full text | |
| Author(s): |
Martinazzo, Ana
;
Espadoto, Mateus
;
Hirata, Nina S. T.
;
IEEE COMP SOC
Total Authors: 4
|
| Document type: | Journal article |
| Source: | 2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR); v. N/A, p. 7-pg., 2021-01-01. |
| Abstract | |
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) | |
| FAPESP's process: | 17/25835-9 - Understanding images and deep learning models |
| Grantee: | Nina Sumiko Tomita Hirata |
| Support Opportunities: | Research Grants - Research Partnership for Technological Innovation - PITE |
| FAPESP's process: | 18/25671-9 - Astronomical image processing and analysis using deep convolutional models |
| Grantee: | Ana Carolina Rodrigues Cavalcante Martinazzo |
| Support Opportunities: | Scholarships in Brazil - Master |