论文标题
在ARPES中的自我监督学习的转移学习应用
Transfer Learning Application of Self-supervised Learning in ARPES
论文作者
论文摘要
角度分辨光发射光谱（ARPES）技术的最新发展涉及空间解决样品，同时保持动量空间的高分辨率特征。这种开发很容易扩大数据大小及其复杂性，以进行数据分析，其中之一是标记相似的分散量并在空间上绘制它们。在这项工作中，我们证明了代表性学习（自我监督学习）模型的最新发展与K-均值聚类相结合可以帮助自动化数据分析的一部分并节省宝贵的时间，尽管表现较低。最后，我们在代表空间中介绍了一些射击学习（k-nearest邻居或KNN），在该空间中，我们有选择地为每个已知标签选择一个（k = 1）图像参考，随后将其余数据标记为最近的参考图像。最后一种方法证明了自我监督学习的强度，特别是在ARPE中自动化图像分析，并且可以将其推广到任何涉及图像数据的科学数据分析中。
Recent development in angle-resolved photoemission spectroscopy (ARPES) technique involves spatially resolving samples while maintaining the high-resolution feature of momentum space. This development easily expands the data size and its complexity for data analysis, where one of it is to label similar dispersion cuts and map them spatially. In this work, we demonstrate that the recent development in representational learning (self-supervised learning) model combined with k-means clustering can help automate that part of data analysis and save precious time, albeit with low performance. Finally, we introduce a few-shot learning (k-nearest neighbour or kNN) in representational space where we selectively choose one (k=1) image reference for each known label and subsequently label the rest of the data with respect to the nearest reference image. This last approach demonstrates the strength of the self-supervised learning to automate the image analysis in ARPES in particular and can be generalized into any science data analysis that heavily involves image data.
