论文标题
Pri-Vae：符合条件的信息变异自动编码器
PRI-VAE: Principle-of-Relevant-Information Variational Autoencoders
论文作者
论文摘要
尽管已经做出了重大努力，以学习各种自动编码器（VAE）框架下的分离表示形式，但学习大多数VAE模型动态的基本属性仍然未知和不足。在这项工作中，我们首先提出了一个新颖的学习目标，称其为相关的信息变化自动编码器（PRI-VAE），以学习解开的表示。然后，我们提出了一种信息理论的观点，可以通过检查跨培训时代的一些关键信息理论数量的演变来分析现有的VAE模型。我们的观察结果揭示了与VAE相关的一些基本属性。经验结果还证明了Pri-vae对四个基准数据集的有效性。
Although substantial efforts have been made to learn disentangled representations under the variational autoencoder (VAE) framework, the fundamental properties to the dynamics of learning of most VAE models still remain unknown and under-investigated. In this work, we first propose a novel learning objective, termed the principle-of-relevant-information variational autoencoder (PRI-VAE), to learn disentangled representations. We then present an information-theoretic perspective to analyze existing VAE models by inspecting the evolution of some critical information-theoretic quantities across training epochs. Our observations unveil some fundamental properties associated with VAEs. Empirical results also demonstrate the effectiveness of PRI-VAE on four benchmark data sets.
