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

@inproceedings{Daras2022ScoreGuidedIL, title={Score-Guided Intermediate Layer Optimization: Fast Langevin Mixing for Inverse Problems}, author={Giannis Daras and Yuval Dagan and Alexandros G. Dimakis and Constantinos Daskalakis}, booktitle={International Conference on Machine Learning}, year={2022} }

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…

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