使用自监督表示学习从图像估计银河距离
Estimating Galactic Distances From Images Using Self-supervised Representation Learning
摘要：
我们使用一个对比的自我监督学习框架来估计距离星系的光度图像。我们结合了计算机视觉的数据增强以及银河尘埃的特定应用增强。我们发现，由此产生的视觉表示的星系图像是语义上有用的，并允许快速相似性搜索，并可以成功地微调红移估计的任务。我们表明，（1）在大型未标记数据语料库上进行预训练，然后对一些标签进行微调，可以达到完全监督模型的准确性，这需要2- 4倍的标记数据，以及（2）通过使用斯隆数字巡天（SDSS）的主要银河样本中的所有可用数据标签微调我们的自监督表示，我们优于最先进的监督学习方法。
英文摘要：
We use a contrastive self-supervised learning framework to estimate distances to galaxies from their photometric images. We incorporate data augmentations from computer vision as well as an application-specific augmentation accounting for galactic dust. We find that the resulting visual representations of galaxy images are semantically useful and allow for fast similarity searches, and can be successfully fine-tuned for the task of redshift estimation. We show that (1) pretraining on a large corpus of unlabeled data followed by fine-tuning on some labels can attain the accuracy of a fully-supervised model which requires 2-4x more labeled data, and (2) that by fine-tuning our self-supervised representations using all available data labels in the Main Galaxy Sample of the Sloan Digital Sky Survey (SDSS), we outperform the state-of-the-art supervised learning method.
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