# Leveraging Internal Representations of Model for Magnetic Image Classification

Source: [Leveraging Internal Representations of Model for Magnetic Image Classification](<https://www.sota2.com/research/paper/68fa72751e209131cea567b0>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:26:06.010Z

Authors: Adarsh N L, Arun P V, Alok Porwal, Malcolm Aranha

Published: 2024-03-11T15:15:50.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2403.06797>)

[PDF](<https://arxiv.org/pdf/2403.06797.pdf>)