Training Variational Auto Encoders with Discrete Latent Representations using Importance Sampling
The Variational Auto Encoder (VAE) is a popular generative latent variable model that is often applied for representation learning. Standard VAEs assume continuous valued latent variables and are trained by maximization of the evidence lower bound (ELBO)... (read more)
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Fashion-MNIST
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