Core Concepts
Proposing a model-agnostic posterior approximation method for VAE inference to improve efficiency and accuracy.
Abstract
The content discusses the challenges in training Variational Autoencoders (VAEs) due to poor approximation of latent code posteriors. It introduces a novel approach, Model-Agnostic Posterior Approximation (MAPA), that independently trains generative and inference models. By approximating the true model's posterior, MAPA aims to enhance VAE inference efficiency. The method is demonstrated on low-dimensional synthetic data, showing promising results in capturing posterior trends and improving density estimation with reduced computation.
Stats
Iterative training is inefficient, leading to local optima issues.
MAPA captures the trend of true posteriors.
MAPA-based inference method performs better with less computation than baselines.
Quotes
"We suggest an alternative VAE inference algorithm that trains the generative and inference models independently."
"MAPA resembles a Kernel Density Estimator (KDE)."
"MAPA outperforms baselines on density estimation across different S."