# Self-supervised Learning for Astronomical Image Classification

Source: [Self-supervised Learning for Astronomical Image Classification](<https://www.sota2.com/research/paper/68fa67c91e209131cea18625>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:22:05.587Z

Authors: Ana Martinazzo, Mateus Espadoto, Nina S. T. Hirata

Published: 2020-04-23T17:32:19.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2004.11336>)

[PDF](<https://arxiv.org/pdf/2004.11336.pdf>)