Variational Auto-encoders (VAEs) are deep generative latent variable models that are widely used for a number of downstream tasks. While it has been demonstrated that VAE training can suffer from a number of pathologies, existing literature lacks characterizations of exactly when these pathologies occur and how they impact down-stream task performance. In this paper we concretely characterize conditions under which VAE training exhibits pathologies and connect these failure modes to undesirable effects on specific downstream tasks, such as learning compressed and disentangled representations, adversarial robustness and semi-supervised learning.

翻译：变化式自动编码器(VAE)是广泛用于一些下游任务的深层基因潜在变异模型,虽然已经证明VAE培训可能受到若干病理的困扰,但现有文献没有描述这些病理发生的确切时间以及它们如何影响下游任务绩效。本文具体描述了VAE培训展示病理并将这些失败模式与特定下游任务的不良后果联系起来的条件,例如学习压缩和分解的表征、对抗性强健和半监督学习。