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arXiv Detail & Related papers (2025-06-18T11:59:15Z)
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Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces [0.0]
We develop a theoretical formulation for arbitrary discrete-state Markov processes in the forward diffusion process. As an example, we introduce Blackout Diffusion'', which learns to produce samples from an empty image instead of from noise.
arXiv Detail & Related papers (2023-05-18T16:24:12Z)
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This paper studies score approximation, estimation, and distribution recovery of diffusion models, when data are supported on an unknown low-dimensional linear subspace. We show that with a properly chosen neural network architecture, the score function can be both accurately approximated and efficiently estimated. The generated distribution based on the estimated score function captures the data geometric structures and converges to a close vicinity of the data distribution.
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An optimal control perspective on diffusion-based generative modeling [9.806130366152194]
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arXiv Detail & Related papers (2022-11-02T17:59:09Z)
Diffusion Models in Vision: A Survey [73.10116197883303]
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arXiv Detail & Related papers (2022-09-10T22:00:30Z)
Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contraction [31.61199061999173]
Diffusion models have a critical downside - they are inherently slow to sample from, needing few thousand steps of iteration to generate images from pure Gaussian noise. We show that starting from Gaussian noise is unnecessary. Instead, starting from a single forward diffusion with better initialization significantly reduces the number of sampling steps in the reverse conditional diffusion. New sampling strategy, dubbed ComeCloser-DiffuseFaster (CCDF), also reveals a new insight on how the existing feedforward neural network approaches for inverse problems can be synergistically combined with the diffusion models.
arXiv Detail & Related papers (2021-12-09T04:28:41Z)

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