Influpaint uses generative diffusion models on image-encoded influenza data to produce realistic and diverse epidemic trajectories that match leading ensemble methods in accuracy.
A theoretical justification for image inpainting using denoising diffusion probabilistic model s
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A task-aware flow-based generative framework optimizes subsampling masks in compressed sensing, reporting SOTA PSNR of 25.17 dB at 5% rate on CelebA and 29.24 dB for 8x MRI on fastMRI.
A survey that introduces taxonomies for categorizing pre-trained diffusion model methods applied to inverse problems and analyzes their connections and challenges.
citing papers explorer
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Generative diffusion models for spatiotemporal influenza forecasting
Influpaint uses generative diffusion models on image-encoded influenza data to produce realistic and diverse epidemic trajectories that match leading ensemble methods in accuracy.
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Flow-Based Generative Modeling for Optimizing Sampling Policies in Compressed Sensing Applications
A task-aware flow-based generative framework optimizes subsampling masks in compressed sensing, reporting SOTA PSNR of 25.17 dB at 5% rate on CelebA and 29.24 dB for 8x MRI on fastMRI.
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A Survey on Diffusion Models for Inverse Problems
A survey that introduces taxonomies for categorizing pre-trained diffusion model methods applied to inverse problems and analyzes their connections and challenges.