Latent Dataset Distillation with Diffusion Models

arXiv:2403.03881v1 Announce Type: cross
Abstract: The efficacy of machine learning has traditionally relied on the availability of increasingly larger datasets. However, large datasets pose storage challenges and contain non-influential samples, which could be ignored during training without impacting the final accuracy of the model. In response to these limitations, the concept of distilling the information on a dataset into a condensed set of (synthetic) samples, namely a distilled dataset, emerged. One crucial aspect is the selected architecture (usually ConvNet) for …

abstract accuracy arxiv availability challenges concept cs.ai cs.cv cs.lg dataset datasets diffusion diffusion models distillation however large datasets limitations machine machine learning samples storage training type