论文标题
通过流动拓扑评估深层生成模型的解开
Evaluating the Disentanglement of Deep Generative Models through Manifold Topology
论文作者
论文摘要
学习分解的表示被认为是改善生成模型的概括，鲁棒性和解释性的基本任务。但是，测量分离是具有挑战性且不一致的，通常取决于临时外部模型或特定于某个数据集。为了解决这个问题，我们提出了一种量化仅使用生成模型的分离的方法，该方法是通过测量学习表示中有条件亚策略的拓扑相似性来量化的。该方法展示了无监督和监督的变体。为了说明我们方法的有效性和适用性，我们经验评估了多个数据集的几个最先进模型。我们发现我们的方法将与现有方法类似的模型排名。我们在https://github.com/stanfordmlgroup/disentangemlement上公开提供我们的代码。
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.
