Dataset Augmentation

Dataset augmentation, the process of expanding training datasets with synthetically generated data, aims to improve the performance and robustness of machine learning models, particularly when real-world data is scarce, biased, or expensive to collect. Current research focuses on leveraging generative models like diffusion models and transformers to create realistic synthetic data for various domains, including natural language processing, image recognition, and even quantum computing. These techniques are proving valuable in addressing challenges like class imbalance, bias mitigation, and the development of more accurate and generalizable models across diverse applications, ranging from forensic science to biomedical research.