Dataset Diversity

Dataset diversity, encompassing the variety of features and characteristics within a dataset, is crucial for training robust and generalizable machine learning models, particularly in sensitive domains like healthcare and autonomous driving. Current research focuses on quantifying and measuring diversity using novel metrics beyond simple size and class balance, employing techniques like conditional variational autoencoders for privacy-preserving data augmentation and leveraging large language models for improved data annotation and taxonomy construction. Improving dataset diversity is vital for enhancing model performance, mitigating bias, and ensuring fairness and reliability across diverse real-world applications.