Disentangled Representation Learning aims to improve the explainability of deep learning methods by training a data encoder that identifies semantically meaningful latent variables in the data generation process. Nevertheless, there is no consensus regarding a universally accepted definition for the objective of disentangled representation learning. In particular, there is a considerable amount of discourse regarding whether should the latent variables be mutually independent or not. In this paper, we first investigate these arguments on the interrelationships between latent variables by establishing a conceptual bridge between Epistemology and Disentangled Representation Learning. Then, inspired by these interdisciplinary concepts, we introduce a two-level latent space framework to provide a general solution to the prior arguments on this issue. Finally, we propose a novel method for disentangled representation learning by employing an integration of mutual information constraint and independence constraint within the Generative Adversarial Network (GAN) framework. Experimental results demonstrate that our proposed method consistently outperforms baseline approaches in both quantitative and qualitative evaluations. The method exhibits strong performance across multiple commonly used metrics and demonstrates a great capability in disentangling various semantic factors, leading to an improved quality of controllable generation, which consequently benefits the explainability of the algorithm.

本研究解决了解耦表示学习中对潜变量独立性的争议问题。通过建立一个认识论与解耦表示学习之间的概念桥梁，提出了一个双层潜空间框架，进而实现了一种新颖的方法，该方法集成了互信息约束和独立性约束于生成对抗网络中。实验结果表明，所提方法在定量和定性评估上均优于基线方法，增强了可控生成的质量，从而提升了算法的可解释性。

从认识论角度看独立约束的解耦表示学习