Variational Composite Autoencoders

TL;DR

This paper introduces variational composite autoencoders that improve latent variable modeling by leveraging hierarchical structures, addressing limitations of traditional variational autoencoders in complex data scenarios.

Contribution

It proposes a novel hierarchical variational autoencoder framework that enhances modeling capacity and inference effectiveness over previous methods.

Findings

Demonstrates improved performance on complex data tasks

Shows advantages over traditional variational autoencoders

Validates the approach through experimental results

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.