Generative models revolutionize the approach to compressed sensing by providing a structured prior on signals through their latent spaces. Early work demonstrated that gradient descent on these latent spaces can effectively estimate signals, though challenges like convergence and latency remain. Subsequent research has explored various techniques to enhance the performance and efficiency of these models, paving the way for innovative solutions in complex nonlinear problems.
