Generative Adversarial Networks for Data Augmentation in Low-Resource Machine Learning Applications
Abstract
The performance of machine learning models is often limited by the availability of labeled data, particularly in low-resource domains like healthcare, agriculture, and disaster management. This paper investigates the use of Generative Adversarial Networks (GANs) for synthetic data generation to augment training datasets. By learning the underlying data distribution, GANs produce realistic and diverse samples that enhance model generalization. The proposed approach is evaluated on multiple low-resource datasets, demonstrating significant improvements in classification and prediction tasks. Additionally, the study highlights the role of GANs in mitigating class imbalance, a common issue in real-world datasets. This research underscores the potential of GANs as a cost-effective and efficient solution for addressing data scarcity in machine learning.
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