Current autoencoder-based disentangled representation learning methods achieve disentanglement by penalizing the (aggregate) posterior to encourage statistical independence of the latent factors. This approach introduces a trade-off between disentangled representation learning and reconstruction quality since the model does not have enough capacity to learn correlated latent variables that capture detail information present in most image data. To overcome this trade-off, we present a novel multi-stage modeling approach where the disentangled factors are first learned using a penalty-based disentangled representation learning method; then, the low-quality reconstruction is improved with another deep generative model that is trained to model the missing correlated latent variables, adding detail information while maintaining conditioning on the previously learned disentangled factors. Taken together, our multi-stage modelling approach results in a single, coherent probabilistic model that is theoretically justified by the principal of D-separation and can be realized with a variety of model classes including likelihood-based models such as variational autoencoders, implicit models such as generative adversarial networks, and tractable models like normalizing flows or mixtures of Gaussians. We demonstrate that our multi-stage model has higher reconstruction quality than current state-of-the-art methods with equivalent disentanglement performance across multiple standard benchmarks. In addition, we apply the multi-stage model to generate synthetic tabular datasets, showcasing an enhanced performance over benchmark models across a variety of metrics. The interpretability analysis further indicates that the multi-stage model can effectively uncover distinct and meaningful features of variations from which the original distribution can be recovered.

翻译：基于自编码器的解耦表示学习方法通常通过惩罚（聚合）后验分布来促进潜在因子的统计独立性，从而实现解耦。然而，该方法在解耦表示学习与重建质量之间引入了权衡：模型缺乏足够能力学习捕捉多数图像数据中细节信息的相关潜在变量。为解决此权衡，我们提出了一种新颖的多阶段建模方法：首先利用基于惩罚的解耦表示学习方法学习解耦因子；随后通过另一个深度生成模型改进低质量重建，该模型专门训练以建模缺失的相关潜在变量，在保持对先前学习解耦因子条件依赖的同时添加细节信息。综合而言，我们的多阶段建模方法形成了单一且连贯的概率模型，该模型在理论上基于D-分离原理得到证明，并通过多种模型类别实现，包括基于似然的变分自编码器、隐式生成对抗网络，以及可处理的正则化流或高斯混合模型。实验表明，在多个标准基准测试中，我们的多阶段模型在保持与当前最优方法同等解耦性能的同时，实现了更高的重建质量。此外，我们将多阶段模型应用于合成表格数据生成，在多种评估指标上展现出优于基准模型的性能。可解释性分析进一步表明，该模型能有效揭示原始分布中不同且具有意义的变异特征，并据此重构原始分布。