A Comprehensive Study on Data Augmentation using Machine Learning with Variational Autoencoders

Data augmentation plays a pivotal role in enhancing the performance of machine learning models, particularly in tasks where labeled data is limited. This paper introduces an innovative approach to data augmentation utilizing Variational Autoencoders (VAEs). VAEs are generative models that learn the underlying structure of the input data, allowing for the generation of new, similar data points. In this paper, we explore the theoretical foundations of VAEs, their application in data augmentation, and present a detailed study on the challenges of this approach in a real-world scenario.

I. Introduction:

Machine learning models heavily rely on large and diverse datasets for training. However, acquiring labeled data for training purposes is often expensive and time-consuming. Data augmentation emerges as a potent solution to mitigate the data scarcity dilemma [1]. By artificially enlarging the dataset, machine learning practitioners provide models with a richer and more varied set of examples, essential for robust learning. Traditional augmentation techniques like rotation, translation, and flipping have been the go-to methods, yet they possess limitations in capturing the intricacies and nuances of complex data distributions [1].