论文标题
变异自动编码器的故障模式及其对下游任务的影响
Failure Modes of Variational Autoencoders and Their Effects on Downstream Tasks
论文作者
论文摘要
变分自动编码器（VAE）是深生成潜在变量模型，可广泛用于许多下游任务。尽管已经证明VAE训练可能患有多种病理，但现有文献缺乏确切何时发生这些病理的特征以及它们如何影响下游任务绩效。在本文中，我们具体表征了VAE训练表现出病理的条件，并将这些故障模式连接到对特定的下游任务的不良影响，例如学习压缩和分解的表示，对抗性鲁棒性以及半监督的学习。
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 downstream 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.
