Deep latent variable models
Deep latent variable models assume a generative process whereby a simple random variable is transformed from the latent space to the observed, output space through a deep neural network. Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE) are two of the most popular variants of this approach
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