Adrián Rodríguez-Muñoz

Ambient Dataloops: Generative Models for Dataset Refinement

Ambient Dataloops is an iterative framework for refining datasets that makes it easier for diffusion models to learn the underlying data distribution, and a dataset-model co-evolution process; at each iteration of the method, the dataset becomes progressively higher quality, and the model improves accordingly.