Deep learning is successful in many fields due to its ability to learn strong feature representations without the need for hand-crafted features, resulting in models with high representational power. However, many of these models are based on supervised learning and therefore depend on the availability of large annotated data sets. These are often difficult to obtain because they require human input. A common challenge for researchers in space weather is the sparsity of annotations in many of the available data sets, which are either unlabeled or have ambiguous labels. To alleviate the data bottleneck of loosely annotated data sets, unsupervised deep learning has become an important strategy, with anomaly detection being one of the most prominent applications. Unsupervised models have been successfully applied in various domains, such as medical imaging or video surveillance, to distinguish normal from abnormal data. In this work, we investigate how a purely unsupervised approach can be used to detect and extract solar phenomena in extreme ultraviolet images from the NASA SDO spacecraft. We show how a model based on variational autoencoders can be used to detect out-of-distribution samples and to localize regions of interest for solar activity. By using an unsupervised approach, we hope to contribute to space weather monitoring tools and further improve the understanding of space weather drivers.

中文翻译：

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将变分自动编码器应用于全盘太阳图像的无监督异常检测

深度学习在许多领域取得了成功，因为它能够学习强大的特征表示，而无需手工制作特征，从而产生具有高表示能力的模型。然而，其中许多模型都是基于监督学习，因此依赖于大型注释数据集的可用性。这些通常很难获得，因为它们需要人工输入。空间天气研究人员面临的一个常见挑战是许多可用数据集中注释的稀疏性，这些数据集要么没有标签，要么标签不明确。为了缓解松散注释数据集的数据瓶颈，无监督深度学习已成为一种重要策略，其中异常检测是最突出的应用之一。无监督模型已成功应用于医学成像或视频监控等各个领域，以区分正常数据和异常数据。在这项工作中，我们研究了如何使用纯粹无监督的方法来检测和提取 NASA SDO 航天器的极紫外图像中的太阳现象。我们展示了如何使用基于变分自动编码器的模型来检测分布外样本并定位太阳活动感兴趣的区域。通过使用无监督的方法，我们希望为空间天气监测工具做出贡献，并进一步提高对空间天气驱动因素的理解。