Novel Iterative Data Enhancement

Novel iterative data enhancement techniques aim to improve the performance of machine learning models, particularly in low-data regimes, by iteratively refining training datasets. Current research focuses on methods like using teacher models to generate synthetic data based on model errors (e.g., for LLMs) or employing sequential enhancement strategies to improve predictions over time (e.g., in road safety assessment). These advancements are significant because they reduce reliance on large, manually curated datasets, leading to more efficient and scalable model training across diverse applications, from natural language processing to image restoration and road safety analysis.