RDBM reformulates generalized diffusion bridge SDEs to use distribution residuals for adaptive noise modulation, unifying prior bridge models as special cases and achieving SOTA on image restoration tasks.
Pytorch: An imperative style, high-performance deep learning library
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2verdicts
UNVERDICTED 2representative citing papers
Fine-tuning impairs the class balance of foundation models in long-tailed personalized federated learning, which FedPuReL addresses through gradient purification using zero-shot predictions and residual-based personalization to achieve better global and local performance.
citing papers explorer
-
Residual Diffusion Bridge Model for Image Restoration
RDBM reformulates generalized diffusion bridge SDEs to use distribution residuals for adaptive noise modulation, unifying prior bridge models as special cases and achieving SOTA on image restoration tasks.
-
Fine-Tuning Impairs the Balancedness of Foundation Models in Long-tailed Personalized Federated Learning
Fine-tuning impairs the class balance of foundation models in long-tailed personalized federated learning, which FedPuReL addresses through gradient purification using zero-shot predictions and residual-based personalization to achieve better global and local performance.