Importance Weighted Autoencoders

@article{Burda2015ImportanceWA,
  title={Importance Weighted Autoencoders},
  author={Yuri Burda and Roger B. Grosse and Ruslan Salakhutdinov},
  journal={CoRR},
  year={2015},
  volume={abs/1509.00519}
}
The variational autoencoder (VAE; Kingma & Welling (2014)) is a recently proposed generative model pairing a top-down generative network with a bottom-up recognition network which approximates posterior inference. It typically makes strong assumptions about posterior inference, for instance that the posterior distribution is approximately factorial, and that its parameters can be approximated with nonlinear regression from the observations. As we show empirically, the VAE objective can lead to… CONTINUE READING
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