Recent years have seen growing interest in learning disentangled
representations, in which distinct features, such as size or shape, are
represented by distinct neurons. Quantifying the extent to which a given
representation is disentangled is not straightforward; multiple metrics have
been proposed. In this paper, we identify two failings of existing metrics,
which mean they can assign a high score to a model which is still entangled,
and we propose two new metrics, which redress these problems. We then consider
the task of compositional generalization. Unlike prior works, we treat this as
a classification problem, which allows us to use it to measure the
disentanglement ability of the encoder, without depending on the decoder. We
show that performance on this task is (a) generally quite poor, (b) correlated
with most disentanglement metrics, and (c) most strongly correlated with our
newly proposed metrics.

本论文提出两种新的基于分类问题的度量方法来评估编码器的解缠能力，这些度量方法解决了现有度量方法中的两个缺陷，并且发现这些度量方法与组合概括任务的表现强相关。

修正常见解缠度测度中的缺陷

Correcting Flaws in Common Disentanglement Metrics

In this paper, we investigate the problem of learning disentangled
representations. Given a pair of images sharing some attributes, we aim to
create a low-dimensional representation which is split into two parts: a shared
representation that captures the common information between the images and an
exclusive representation that contains the specific information of each image.
To address this issue, we propose a model based on mutual information
estimation without relying on image reconstruction or image generation. Mutual
information maximization is performed to capture the attributes of data in the
shared and exclusive representations while we minimize the mutual information
between the shared and exclusive representation to enforce representation
disentanglement. We show that these representations are useful to perform
downstream tasks such as image classification and image retrieval based on the
shared or exclusive component. Moreover, classification results show that our
model outperforms the state-of-the-art model based on VAE/GAN approaches in
representation disentanglement.

本文研究学习分离表现形式的问题，提出一种基于互信息估计的模型，用于捕捉数据的共享和独占组件，并强制实现表现形式分离，在共享或独占部分基础上实现图像分类和图像检索，结果表明相对于基于 VAE/GAN 方法的最先进模型，本文提出的模型表现更加出色。