论文标题
DCI-ES：与可识别性连接的扩展分离框架
DCI-ES: An Extended Disentanglement Framework with Connections to Identifiability
论文作者
论文摘要
在表示学习中，一种常见的方法是寻求消除差异因素的表示形式。 Eastwood＆Williams（2018）提出了三个指标，以量化此类删除表示形式的质量：删除（D），完整性（C）和信息性（i）。在这项工作中，我们首先将此DCI框架连接到两个通用的线性和非线性可识别性概念，从而在分离和密切相关的独立组件分析领域之间建立了形式的联系。然后，我们提出了一个扩展的DCI -ES框架，具有两个新的表示质量的衡量标准 - 显式性（E）和大小（S） - 并指出如何计算D和C的黑盒预测指标。我们的主要思想是，使用表示形式所需的功能能力是代表质量的重要但因此被忽略的方面，我们使用明确性或易用性（e）对其进行量化。我们说明了我们在MPI3D和CARS3D数据集上扩展的相关性。
In representation learning, a common approach is to seek representations which disentangle the underlying factors of variation. Eastwood & Williams (2018) proposed three metrics for quantifying the quality of such disentangled representations: disentanglement (D), completeness (C) and informativeness (I). In this work, we first connect this DCI framework to two common notions of linear and nonlinear identifiability, thereby establishing a formal link between disentanglement and the closely-related field of independent component analysis. We then propose an extended DCI-ES framework with two new measures of representation quality - explicitness (E) and size (S) - and point out how D and C can be computed for black-box predictors. Our main idea is that the functional capacity required to use a representation is an important but thus-far neglected aspect of representation quality, which we quantify using explicitness or ease-of-use (E). We illustrate the relevance of our extensions on the MPI3D and Cars3D datasets.
