image

Improving relational regularized autoencoders with spherical sliced fused Gromov Wasserstein

Publication Date:

Abstract

Relational regularized autoencoder (RAE) is a framework to learn the distribution of data by minimizing a reconstruction loss together with a relational regularization on the prior of latent space. A recent attempt to reduce the inner discrepancy between the prior and aggregated posterior distributions is to incorporate sliced fused Gromov-Wasserstein (SFG) between these distributions... (read more)

Authors

Topics

https://openreview.net/pdf?id=DiQD7FWL233

0001-01-01 -