With high quality image generation achieved, the next critical step is to enhance user controllability.

We next explain the rationality of the resampling-by-compatibility presented in Sect. We first i) prove Eq. For Assumption (9), L( π, u, v) is strongly convex as proved in [5]. Eq. (A-19), we have E The codes are in pytorch [7]. The learning rate is 1e-5.

On one hand, as other data-driven deep learning methods, our method, namely variational denoising network (VDN), can perform denoising efficiently due to its explicit form of posterior expression. On the other hand, VDN inherits the advantages of traditional model-driven approaches, especially the good generalization capability of generative models.

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