“…Observed Without With Recently, deep-learning-based models have shown great promise for augmenting various inverse problems [3,9,20,32,36,38,42,47,50,53,54,58,59,64]. In particular, deep generative models, such as VAEs [29], GANs [19], and normalizing flows [15,16,28,48], which directly learn from training data distributions, are a powerful and versatile prior.…”