论文标题
Gromov-Wasserstein自动编码器
Gromov-Wasserstein Autoencoders
论文作者
论文摘要
分流自动编码器（VAE）基于生成模型通过合并元元素（一般前提）被认为对下游任务有益的一般前提来提供灵活的表示学习。但是，综合元元素通常涉及与原始似然体系结构的临时模型偏差，从而导致其训练不良变化。在本文中，我们提出了一种新颖的表示学习方法Gromov-Wasserstein自动编码器（GWAE），该方法使用变分自动编码方案直接与潜在和数据分布匹配。 GWAE模型不是基于可能性的目标，而是将Gromov-Wasserstein（GW）指标最小化，可训练的先验和给定数据分布之间。 GW度量可以测量分布之间以距离结构为导向的差异，即使具有不同的维度，这提供了潜在和数据空间之间的直接度量。通过限制先前的家庭，我们可以在不改变其目标的情况下将元重要人介绍给潜在空间。与基于VAE的模型的经验比较表明，GWAE模型在两个突出的元元数据中起作用，分解和聚类，其GW目标不变。
Variational Autoencoder (VAE)-based generative models offer flexible representation learning by incorporating meta-priors, general premises considered beneficial for downstream tasks. However, the incorporated meta-priors often involve ad-hoc model deviations from the original likelihood architecture, causing undesirable changes in their training. In this paper, we propose a novel representation learning method, Gromov-Wasserstein Autoencoders (GWAE), which directly matches the latent and data distributions using the variational autoencoding scheme. Instead of likelihood-based objectives, GWAE models minimize the Gromov-Wasserstein (GW) metric between the trainable prior and given data distributions. The GW metric measures the distance structure-oriented discrepancy between distributions even with different dimensionalities, which provides a direct measure between the latent and data spaces. By restricting the prior family, we can introduce meta-priors into the latent space without changing their objective. The empirical comparisons with VAE-based models show that GWAE models work in two prominent meta-priors, disentanglement and clustering, with their GW objective unchanged.
