  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.
