CzSL：在天文图像分类中从公民科学、专家与未标注数据中学习
CzSL: Learning from citizen science, experts and unlabelled data in astronomical image classification
AI总结：
针对公民科学天文图像标注质量不足、现有方法未充分利用专家标注与未标注数据的问题，提出CzSL方法，结合卷积自编码器与CNN的预训练微调，在Galaxy Zoo数据集上提升了分类性能。
摘要：
公民科学作为一种由公众为海量天文图像集标注的实用工具，正日益普及。但这往往以业余参与者标注的分类质量较差为代价，通常需要使用专业天文学家标注的较小数据集来验证这些标注。尽管公民科学已取得成效，但仅靠它无法应对当前及未来巡天项目的分类需求。为缓解这一问题，公民科学项目已与机器学习技术结合，以追求更可靠的自动化分类。然而，现有方法忽略了一个事实：除了业余爱好者标注的数据，（有限的）专家相关问题知识也可利用，同时还有大量未标注数据尚未在统一学习框架中得到挖掘。本文提出了一种创新的公民科学学习方法，能够同时利用专家标注和业余标注数据，其特点是实现了专家与业余标注之间的标签迁移。该方法首先通过卷积自编码器从未标注数据中学习，随后分别通过卷积神经网络的预训练和微调来利用业余标注与专家标注。我们聚焦于Galaxy Zoo项目的星系图像分类，测试了二分类、多分类以及类别不平衡分类场景。结果表明，与一系列基线方法相比，我们的方案能够提升分类性能，为从不同置信度的标注数据中学习提供了一种有前景的方法。
英文摘要：
Citizen science is gaining popularity as a valuable tool for labelling large collections of astronomical images by the general public. This is often achieved at the cost of poorer quality classifications made by amateur participants, which are usually verified by employing smaller data sets labelled by professional astronomers. Despite its success, citizen science alone will not be able to handle the classification of current and upcoming surveys. To alleviate this issue, citizen science projects have been coupled with machine learning techniques in pursuit of a more robust automated classification. However, existing approaches have neglected the fact that, apart from the data labelled by amateurs, (limited) expert knowledge of the problem is also available along with vast amounts of unlabelled data that have not yet been exploited within a unified learning framework. This paper presents an innovative learning methodology for citizen science capable of taking advantage of expert- and amateur-labelled data, featuring a transfer of labels between experts and amateurs. The proposed approach first learns from unlabelled data with a convolutional autoencoder and then exploits amateur and expert labels via the pre-training and fine-tuning of a convolutional neural network, respectively. We focus on the classification of galaxy images from the Galaxy Zoo project, from which we test binary, multi-class, and imbalanced classification scenarios. The results demonstrate that our solution is able to improve classification performance compared to a set of baseline approaches, deploying a promising methodology for learning from different confidence levels in data labelling.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
