Reviews: Learning Hierarchical Priors in VAEs

This paper discussed how to enhance the existing methods in which designed prior could over regularize the posteriori, so it will try to find a way to learn a complex prior which can learn the latent pattern of data manifold more efficiently. To learn such prior, paper adopted and modified one dual optimization technique and introduced an efficient algorithm on how to update the hierarchical prior and posteriori parameters. The combination of complex priori with the introduced algorithm have learned a posterior which has more informative latent representation and avoids posteriori collapse. In addition, paper introduced a graph search method to interpolate the states and showed how effective algorithm can discover a meaningful posteriori over the experiment section. So we can summarize the contribution of this paper as following - Introduce a hierarchical prior which can avoid over regularization of the posterior while learning latent variables manifold - Adopting and expanding an optimization technique and an algorithm to learn hierarchical prior and hierarchical posterior parameters.