Self-supervised Learning for Astronomical Image Classification

被引：9

作者：

Martinazzo, Ana ^{[1
]}

Espadoto, Mateus ^{[1
]}

Hirata, Nina S. T. ^{[1
]}

机构：

[1] Univ Sao Paulo, Inst Math & Stat, Sao Paulo, Brazil

来源：

2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) | 2021年

基金：

巴西圣保罗研究基金会;

关键词：

NEURAL-NETWORKS; GALAXY; MORPHOLOGY;

D O I：

10.1109/ICPR48806.2021.9412911

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：4176 / 4182

页数：7

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