LaDDer: Latent Data Distribution Modelling with a Generative Prior
This addresses the challenge of better representation learning in generative models for machine learning researchers, but it appears incremental as it builds on existing VAE frameworks.
The paper tackles the problem of accurately modeling the latent data distribution in variational autoencoders to improve representation learning, proposing LaDDer which uses a meta-embedding concept with multiple VAEs and a non-parametric mixture prior, resulting in improved representation quality as shown in experiments.
In this paper, we show that the performance of a learnt generative model is closely related to the model's ability to accurately represent the inferred \textbf{latent data distribution}, i.e. its topology and structural properties. We propose LaDDer to achieve accurate modelling of the latent data distribution in a variational autoencoder framework and to facilitate better representation learning. The central idea of LaDDer is a meta-embedding concept, which uses multiple VAE models to learn an embedding of the embeddings, forming a ladder of encodings. We use a non-parametric mixture as the hyper prior for the innermost VAE and learn all the parameters in a unified variational framework. From extensive experiments, we show that our LaDDer model is able to accurately estimate complex latent distribution and results in improvement in the representation quality. We also propose a novel latent space interpolation method that utilises the derived data distribution.