![]() These models have been highly successful in modeling complex natural distributions such as natural images. Training by maximum likelihood is intractable, so a parametric approximate inference distribution is jointly trained, and surprisingly, jointly training the generative model for maximum likelihood, and the inference distribution to approximate the true posterior is tractable, through a “reparameterization trick”. These models map a prior on latent variables to conditional distributions on the input space. ![]() Variational Autoencoder is a very important class of models in Generative Models. These models have been highly successful in various tasks such as semi-supervised learning, missing data imputation, and generation of novel data samples. Generative Models have been highly successful in a wide variety of tasks by generating new observations from an existing probability density function.
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