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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 spe
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