• 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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14 Citations
Background Citations
10
Methods Citations
3

14 Citations

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References

SHOWING 1-10 OF 27 REFERENCES

Representing Closed Transformation Paths in Encoded Network Latent Space

This work incorporates a generative manifold model into the latent space of an autoencoder in order to learn the low-dimensional manifold structure from the data and adapt the latentspace to accommodate this structure.

The Riemannian Geometry of Deep Generative Models

The Riemannian geometry of these generated manifolds is investigated and it is shown how parallel translation can be used to generate analogies, i.e., to transport a change in one data point into a semantically similar change of another data point.

Metrics for Deep Generative Models

The method yields a principled distance measure, provides a tool for visual inspection of deep generative models, and an alternative to linear interpolation in latent space and can be applied for robot movement generalization using previously learned skills.

Latent Space Oddity: on the Curvature of Deep Generative Models

This work shows that the nonlinearity of the generator imply that the latent space gives a distorted view of the input space, and shows that this distortion can be characterized by a stochastic Riemannian metric, and demonstrates that distances and interpolants are significantly improved under this metric.

Variational Diffusion Autoencoders with Random Walk Sampling

A principled measure for recognizing the mismatch between data and latent distributions and a method that combines the advantages of variational inference and diffusion maps to learn a homeomorphic generative model are proposed.

Hyperspherical Variational Auto-Encoders

This work proposes using a von Mises-Fisher distribution instead of a Gaussian distribution for both the prior and posterior of the Variational Auto-Encoder, leading to a hyperspherical latent space.

Variational Autoencoders with Riemannian Brownian Motion Priors

This work assumes a Riemannian structure over the latent space, which constitutes a more principled geometric view of the latent codes, and replaces the standard Gaussian prior with a R Siemannian Brownian motion prior, and demonstrates that this prior significantly increases model capacity using only one additional scalar parameter.

Mixed-curvature Variational Autoencoders

A Mixed-curvature Variational Autoencoder is developed, an efficient way to train a VAE whose latent space is a product of constant curvature Riemannian manifolds, where the per-component curvature is fixed or learnable.

Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders

This work endow VAEs with a Poincare ball model of hyperbolic geometry as a latent space and rigorously derive the necessary methods to work with two main Gaussian generalisations on that space.

Importance Weighted Autoencoders

The importance weighted autoencoder (IWAE), a generative model with the same architecture as the VAE, but which uses a strictly tighter log-likelihood lower bound derived from importance weighting, shows empirically that IWAEs learn richer latent space representations than VAEs, leading to improved test log- likelihood on density estimation benchmarks.