2020/01/31 by Abhishek Kumar, Kumar, Abhishek, Ben Poole +1 · 2 citations
Computer Science · Mathematics · Physics and Astronomy · Social Sciences · #Computational and Text Analysis Methods #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks #Music and Audio Processing #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2002.00041
ICML 2020; Final version, including appendix
openalex publication_date 2020/01/31 · arxiv created 2020/12/28 · arxiv updated 2021/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
While the impact of variational inference (VI) on posterior inference in a fixed generative model is well-characterized, its role in regularizing a learned generative model when used in variational autoencoders (VAEs) is poorly understood. We study the regularizing effects of variational distributions on learning in generative models from two perspectives. First, we analyze the role that the choice of variational family plays in imparting uniqueness to the learned model by restricting the set of optimal generative models. Second, we study the regularization effect of the variational family on the local geometry of the decoding model. This analysis uncovers the regularizer implicit in the β-VAE objective, and leads to an approximation consisting of a deterministic autoencoding objective plus analytic regularizers that depend on the Hessian or Jacobian of the decoding model, unifying VAEs with recent heuristics proposed for training regularized autoencoders. We empirically verify these findings, observing that the proposed deterministic objective exhibits similar behavior to the β-VAE in terms of objective value and sample quality.