3 papers
cs.LG2026
Ambient Dataloops: Generative Models for Dataset Refinement
Adrián RodrÃguez-Muñoz, William Daspit, Adam Klivans +3
We propose Ambient Dataloops, an iterative framework for refining datasets that makes it easier for diffusion models to learn the underlying data distribution. Modern datasets cont…
cs.CV2025
Separating Knowledge and Perception with Procedural Data
Adrián RodrÃguez-Muñoz, Manel Baradad, Phillip Isola +1
We train representation models with procedural data only, and apply them on visual similarity, classification, and semantic segmentation tasks without further training by using vis…
cs.LG2024
Characterizing Model Robustness via Natural Input Gradients
Adrián RodrÃguez-Muñoz, Tongzhou Wang, Antonio Torralba
Adversarially robust models are locally smooth around each data sample so that small perturbations cannot drastically change model outputs. In modern systems, such smoothness is us…