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Full-Model Optimality for Tunable Linear Generative Priors in Compressed Sensing
Generative models have been studied experimentally and theoretically as priors for inverse problems such as compressed sensing. Recent work by Gunn et al. studied the use of generative priors with tunable complexity, where a family of generative priors with varying complexity is maintained and a specific complexity can be selected at inversion time. They demonstrated that lower reconstruction errors can be experimentally attained for a variety of inverse problems by appropriately tuning the complexity of the generative prior. In the present paper, we establish theory for compressed sensing in
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- arXiv · AI, language, vision and robotics · 2026-09-02T16:22:26.000Z
First collected: 2026-09-21T05:32:15.665Z. This is not the publication date.