Matching aggregate posteriors in the variational autoencoder

• URL: http://arxiv.org/abs/2311.07693v2
• Date: Fri, 04 Oct 2024 23:28:46 GMT
• Title: Matching aggregate posteriors in the variational autoencoder
• Authors: Surojit Saha, Sarang Joshi, Ross Whitaker,
• Abstract summary: The variational autoencoder (VAE) is a well-studied, deep, latent-variable model (DLVM) This paper addresses shortcomings in VAEs by reformulating the objective function associated with VAEs in order to match the aggregate/marginal posterior distribution to the prior. The proposed method is named the emphaggregate variational autoencoder (AVAE) and is built on the theoretical framework of the VAE.
• Score: 0.5759862457142761
• License:
• Abstract: The variational autoencoder (VAE) is a well-studied, deep, latent-variable model (DLVM) that efficiently optimizes the variational lower bound of the log marginal data likelihood and has a strong theoretical foundation. However, the VAE's known failure to match the aggregate posterior often results in \emph{pockets/holes} in the latent distribution (i.e., a failure to match the prior) and/or \emph{posterior collapse}, which is associated with a loss of information in the latent space. This paper addresses these shortcomings in VAEs by reformulating the objective function associated with VAEs in order to match the aggregate/marginal posterior distribution to the prior. We use kernel density estimate (KDE) to model the aggregate posterior in high dimensions. The proposed method is named the \emph{aggregate variational autoencoder} (AVAE) and is built on the theoretical framework of the VAE. Empirical evaluation of the proposed method on multiple benchmark data sets demonstrates the effectiveness of the AVAE relative to state-of-the-art (SOTA) methods.

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