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.