论文标题
基于线性对称性的分离的度量标准
A Metric for Linear Symmetry-Based Disentanglement
论文作者
论文摘要
（Higgins等，2018）提出的基于线性对称性的分解（LSBD）的定义概述了应表征捕获数据对称性的分离表示的属性。但是，尚不清楚如何衡量数据表示符合这些属性的程度。我们提出了一个评估数据表示达到LSBD水平的度量。我们提供了一种评估该指标的实用方法，并使用它来评估针对三个具有基础$ SO（2）$ symmetries的数据集获得的数据表示的分离。
The definition of Linear Symmetry-Based Disentanglement (LSBD) proposed by (Higgins et al., 2018) outlines the properties that should characterize a disentangled representation that captures the symmetries of data. However, it is not clear how to measure the degree to which a data representation fulfills these properties. We propose a metric for the evaluation of the level of LSBD that a data representation achieves. We provide a practical method to evaluate this metric and use it to evaluate the disentanglement of the data representations obtained for three datasets with underlying $SO(2)$ symmetries.
