Abstract
Learning in the latent variable model is challenging in the presence of the
complex data structure or the intractable latent variable. Previous variational
autoencoders can be low effective due to the straightforward encoder-decoder
structure. In this paper, we propose a variational composite autoencoder to
sidestep this issue by amortizing on top of the hierarchical latent variable
model. The experimental results confirm the advantages of our model.
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