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.
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by Yaniv Yacoby... at arxiv.org 03-15-2024
https://arxiv.org/pdf/2403.08941.pdfDeeper Inquiries