Data Enhancement

Data enhancement techniques aim to improve the performance of machine learning models by addressing limitations in the available training data, such as insufficient volume or class imbalance. Current research focuses on developing methods to augment data for various model architectures, including graph neural networks and deep learning models like U-Net, often employing transfer learning and large language models to generate synthetic data or improve existing datasets. These advancements are crucial for improving the accuracy and reliability of machine learning applications across diverse fields, from medical image analysis to manufacturing quality control, particularly where obtaining large, high-quality datasets is challenging or expensive.