Independence Constrained Disentangled Representation Learning from Epistemological Perspective

TL;DR

This paper explores the philosophical foundations of disentangled representation learning, proposing a novel GAN-based method that enforces independence constraints to improve interpretability and semantic disentanglement.

Contribution

It introduces a two-level latent space framework inspired by epistemology and develops a new GAN method combining mutual information and independence constraints.

Findings

Outperforms baseline methods in quantitative metrics

Achieves better semantic disentanglement

Enhances controllable data generation

Abstract

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.