Informative Subset

Informative subset selection focuses on identifying smaller, representative portions of large datasets that retain crucial information for efficient model training and inference. Current research emphasizes developing algorithms that consistently perform well across various dataset sizes and model architectures, often incorporating uncertainty estimation and active learning techniques to guide the selection process. This field is crucial for mitigating the computational cost and environmental impact of training large models, particularly in domains like image classification, natural language processing, and remote sensing, where datasets are often massive. Improved subset selection methods promise significant advancements in efficiency and scalability for various machine learning applications.