mlsanityData to 14 Oct 2026

Score-Guided Intermediate Layer Optimization: Fast Langevin Mixing for Inverse Problems

arXiv 2206.09104 ↗First posted 18 Jun 2022cs.LG cs.AIcited by 11

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Abstract (arXiv)

We prove fast mixing and characterize the stationary distribution of the Langevin Algorithm for inverting random weighted DNN generators. This result extends the work of Hand and Voroninski from efficient inversion to efficient posterior sampling. In practice, to allow for increased expressivity, we propose to do posterior sampling in the latent space of a pre-trained generative model. To achieve that, we train a score-based model in the latent space of a StyleGAN-2 and we use it to solve inverse problems. Our framework, Score-Guided Intermediate Layer Optimization (SGILO), extends prior work by replacing the sparsity regularization with a generative prior in the intermediate layer. Experimentally, we obtain significant improvements over the previous state-of-the-art, especially in the low measurement regime.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 61 of its 74 references that are in the map. First posted on arXiv 18 Jun 2022.

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61 of 74 references.

References · 61

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38 more
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Citing papers per year: 2022: 3; 2023: 5; 2024: 2; 2025: 1.

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  4. 2023A Complete Recipe for Diffusion Generative Models
  5. 2026Non-asymptotic Convergence of Stochastic Gradient Descent in Score-based Generative Models
  6. 2025Preconditioned Langevin Dynamics with Score-Based Generative Models for Infinite-Dimensional Linear Bayesian Inverse Problems
  7. 2024Posterior sampling via Langevin dynamics based on generative priors
  8. 2023Solving Inverse Problems with Score-Based Generative Priors learned from Noisy Data