• Corpus ID: 219792422

Variational Autoencoder with Learned Latent Structure

@article{Connor2020VariationalAW,
  title={Variational Autoencoder with Learned Latent Structure},
  author={Marissa Connor and Gregory H. Canal and Christopher J. Rozell},
  journal={ArXiv},
  year={2020},
  volume={abs/2006.10597}
}
The manifold hypothesis states that high-dimensional data can be modeled as lying on or near a low-dimensional, nonlinear manifold. Variational Autoencoders (VAEs) approximate this manifold by learning mappings from low-dimensional latent vectors to high-dimensional data while encouraging a global structure in the latent space through the use of a specified prior distribution. When this prior does not match the structure of the true data manifold, it can lead to a less accurate model of the… 

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