Learning disentangled representations is regarded as a fundamental task for
improving the generalization, robustness, and interpretability of generative
models. However, measuring disentanglement has been challenging and
inconsistent, often dependent on an ad-hoc external model or specific to a
certain dataset. To address this, we present a method for quantifying
disentanglement that only uses the generative model, by measuring the
topological similarity of conditional submanifolds in the learned
representation. This method showcases both unsupervised and supervised
variants. To illustrate the effectiveness and applicability of our method, we
empirically evaluate several state-of-the-art models across multiple datasets.
We find that our method ranks models similarly to existing methods. We make
ourcode publicly available at
this https URL

本文提出了一种利用生成模型度量解缕编表示的方法。通过量化学习表示中条件子流形的拓扑相似性，该方法不依赖于外部模型或特定数据集即可测量解缕编程度。我们通过多个数据集上的实验表明了该方法的有效性和适用性，发现与现有方法相比，该方法能够很好地对模型进行排序。