Optimizing Data Augmentation Techniques for Improved Generalization in Machine Learning Models

Abstract

In situations when labeled data is limited or unbalanced, data augmentation has become an essential method for improving machine learning models' ability to generalize. In order to make models more resilient and less prone to overfitting, augmentation approaches generate different versions of the training data. better generalization across various machine learning tasks through optimizing data augmentation methodologies. From more conventional approaches like geometric changes and noise injection to cutting-edge methods like adversarial training and neural style transfer, we explore it all. We test several augmentation strategies extensively on image classification, NLP, and time-series prediction tasks to see how they affect model performance. We show that optimum data augmentation, when adjusted for dataset specifics, greatly improves model robustness and accuracy when dealing with out-of-sample data. In addition, we offer a new approach to augmentation that integrates augmentation rules with automated search algorithms, allowing for strategies to be dynamically adjusted throughout training. In practical settings with uneven or scarce data, our findings pave the way for stronger machine learning models.