Characterizing and Avoiding Problematic Global Optima of Variational AutoencodersDownload PDF

16 Oct 2019 (modified: 08 Dec 2019)AABI 2019 Symposium Blind SubmissionReaders: Everyone
  • Keywords: Variational Autoencoders, Variational Inference, VAE, Approximate Inference, Posterior Collapse, Aggregated Posterior
  • TL;DR: We characterize problematic global optima of the VAE objective and present a novel inference method to avoid such optima.
  • Abstract: Variational Auto-encoders (VAEs) are deep generative latent variable models consisting of two components: a generative model that captures a data distribution p(x) by transforming a distribution p(z) over latent space, and an inference model that infers likely latent codes for each data point (Kingma and Welling, 2013). Recent work shows that traditional training methods tend to yield solutions that violate modeling desiderata: (1) the learned generative model captures the observed data distribution but does so while ignoring the latent codes, resulting in codes that do not represent the data (e.g. van den Oord et al. (2017); Kim et al. (2018)); (2) the aggregate of the learned latent codes does not match the prior p(z). This mismatch means that the learned generative model will be unable to generate realistic data with samples from p(z)(e.g. Makhzani et al. (2015); Tomczak and Welling (2017)). In this paper, we demonstrate that both issues stem from the fact that the global optima of the VAE training objective often correspond to undesirable solutions. Our analysis builds on two observations: (1) the generative model is unidentifiable – there exist many generative models that explain the data equally well, each with different (and potentially unwanted) properties and (2) bias in the VAE objective – the VAE objective may prefer generative models that explain the data poorly but have posteriors that are easy to approximate. We present a novel inference method, LiBI, mitigating the problems identified in our analysis. On synthetic datasets, we show that LiBI can learn generative models that capture the data distribution and inference models that better satisfy modeling assumptions when traditional methods struggle to do so.
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