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Diffusion with Forward Models: Solving Stochastic Inverse Problems Without Direct Supervision
The paper presents a new class of conditional denoising diffusion probabilistic models that can sample from distributions of signals that are never observed directly, but only through a known differentiable forward model. The approach is demonstrated on three challenging computer vision tasks.
https://arxiv.org/abs//2306.11719
YouTube: https://www.youtube.com/@ArxivPapers
PODCASTS:
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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By Igor Melnyk
5
33 ratings
The paper presents a new class of conditional denoising diffusion probabilistic models that can sample from distributions of signals that are never observed directly, but only through a known differentiable forward model. The approach is demonstrated on three challenging computer vision tasks.
https://arxiv.org/abs//2306.11719
YouTube: https://www.youtube.com/@ArxivPapers
PODCASTS:
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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