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Found 4 papers, 2 papers with code
Decoupled Data Consistency with Diffusion Purification for Image Restoration
To solve image restoration problems, many existing techniques achieve data consistency by incorporating additional likelihood gradient steps into the reverse sampling process of diffusion models.
Efficient Compression of Overparameterized Deep Models through Low-Dimensional Learning Dynamics
We empirically evaluate the effectiveness of our compression technique on matrix recovery problems.
Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistency
However, training diffusion models in the pixel space are both data-intensive and computationally demanding, which restricts their applicability as priors for high-dimensional real-world data such as medical images.
Low-Rank Phase Retrieval with Structured Tensor Models
We study the low-rank phase retrieval problem, where the objective is to recover a sequence of signals (typically images) given the magnitude of linear measurements of those signals.
