Dataset Distillation

Dataset distillation aims to create smaller, synthetic datasets that retain the essential information of much larger original datasets, thereby reducing computational costs and storage needs for training deep learning models. Current research focuses on improving the efficiency and generalizability of these synthetic datasets across different model architectures, often employing techniques like knowledge distillation, generative models (e.g., diffusion models, GANs), and trajectory matching to achieve this. This field is significant because it addresses the growing challenges of data size and computational expense in deep learning, with potential applications ranging from improving training efficiency to enabling data sharing in resource-constrained environments like TinyML and medical imaging.