Data Augmentation Techniques and Their Effect on Model Robustness in Low-Data Regimes

Abstract

The performance and generalization capabilities of machine learning models, especially deep learning architectures, are highly dependent on the quality and quantity of training data. In scenarios where only limited data is available, models are prone to overfitting and may fail to perform well on unseen data. Data augmentation, the process of artificially increasing the diversity and size of the training dataset through transformations and modifications, has emerged as a key strategy to combat the challenges posed by low-data regimes. This research paper presents a comprehensive analysis of various data augmentation techniques and their impact on improving the robustness of machine learning models under data scarcity. We analyze traditional augmentation methods, including geometric and photometric transformations, as well as advanced techniques such as generative adversarial networks (GANs) and transformer-based augmentation strategies. A series of experiments on benchmark datasets demonstrates that appropriate augmentation can significantly enhance model performance and robustness, particularly when combined with regularization techniques. The findings underscore the role of augmentation not just as a means of increasing data quantity, but as a tool for better generalization, adversarial robustness, and noise resistance.