  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
https://github.com/stanfordmlgroup/disentanglement.
