Data generated by edge devices has the potential to train intelligent autonomous systems across various domains. Despite the emergence of diverse machine learning approaches addressing privacy concerns and utilizing distributed data, security issues persist due to the sensitive storage of data shards in disparate locations. This paper introduces a potentially groundbreaking paradigm for machine learning model training, specifically designed for scenarios with only a single magnetic image and its corresponding label image available. We harness the capabilities of Deep Learning to generate concise yet informative samples, aiming to overcome data scarcity. Through the utilization of deep learning's internal representations, our objective is to efficiently address data scarcity issues and produce meaningful results. This methodology presents a promising avenue for training machine learning models with minimal data.

翻译：边缘设备生成的数据具有训练跨领域智能自主系统的潜力。尽管多种机器学习方法已能解决隐私问题并利用分布式数据，但因数据分片分散存储的敏感性，安全问题依然存在。本文提出一种可能具有开创性的机器学习模型训练范式，专为仅有一张磁图像及其对应标注图像可用的场景设计。我们利用深度学习生成简洁但信息丰富的样本，旨在克服数据稀缺问题。通过运用深度学习的内部表示，我们的目标是高效解决数据稀缺性难题并产生有意义的结果。该方法为使用极少量数据训练机器学习模型提供了一条有前景的路径。