Distilled Dataset

Dataset distillation aims to create significantly smaller, synthetic datasets that retain the essential information of much larger original datasets, enabling faster and more efficient training of machine learning models. Current research focuses on improving the quality and robustness of these distilled datasets, exploring techniques like matching-based methods, diffusion models, and the strategic use of soft labels to address issues such as class imbalance and cross-architecture generalization. This field is significant because it offers solutions to the computational and storage challenges posed by massive datasets, impacting areas like federated learning, resource-constrained applications, and model compression.