The article discusses Bayesian Conditioned Diffusion Models for Inverse Problems, authored by Alper Güngör, Bahri Batuhan Bilecen, and Tolga Çukur. Diffusion models have been effective in image reconstruction tasks involving inverse problems, but current methods often result in suboptimal performance. The authors propose a novel Bayesian conditioning technique, BCDM, for diffusion models to address this issue. This technique is based on score-functions associated with the conditional distribution of desired images given measured data. The theory to express and train the conditional score-function is rigorously derived. The proposed technique shows state-of-the-art performance in image dealiasing, deblurring, super-resolution, and inpainting.
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Source link: https://medium.com/@monocosmo77/dynamics-of-inverse-problems-in-machine-learning-part1-38bdabb56f83?source=rss——artificial_intelligence-5
Exploring the Dynamics of Inverse Problems in Machine Learning #InverseLearning
