Tunable Latent Generative Priors for Compressed Sensing and Inverse Problems
This work develops tunable latent priors for diffusion models, normalizing flows, and variational autoencoders, leveraging nested dropout and shows empirically that tunable priors consistently achieve lower reconstruction errors than fixed-complexity baselines.
Sean Gunn, Jorio Cocola, Oliver De Candido et al.
· 1 citation