Low Quality

Low-quality data, a pervasive challenge across scientific domains, hinders the accuracy and efficiency of machine learning models and other data-driven analyses. Current research focuses on developing methods to enhance low-quality data, including multi-fidelity approaches that combine sparse high-fidelity data with abundant low-fidelity data, and techniques leveraging self-supervised learning, diffusion models, and Bayesian optimization to improve data quality and model robustness. These advancements are crucial for various applications, from improving air quality monitoring and autonomous systems to accelerating scientific discovery in fields like fluid dynamics and medical imaging, where high-fidelity data acquisition is often expensive or impractical.