Data Distillation

Data distillation aims to create smaller, synthetic datasets that retain the performance of much larger original datasets, addressing the high computational costs and resource demands of training large machine learning models. Current research focuses on developing efficient distillation methods for various data types (images, text, signals) and model architectures, often employing techniques like distribution matching, generative models, and soft labels to create high-quality synthetic data. This work is significant because it promises to accelerate model training, improve data privacy, and enable broader access to machine learning by reducing the need for massive datasets.