Disentangled Representation Learning via Flow Matching

• URL: http://arxiv.org/abs/2602.05214v1
• Date: Thu, 05 Feb 2026 02:14:36 GMT
• Title: Disentangled Representation Learning via Flow Matching
• Authors: Jinjin Chi, Taoping Liu, Mengtao Yin, Ximing Li, Yongcheng Jing, Dacheng Tao,
• Abstract summary: Disentangled representation learning aims to capture the underlying explanatory factors of observed data.<n>Existing diffusion-based methods encourage factor independence via inductive biases, yet frequently lack strong semantic alignment.<n>We propose a flow matching-based framework for disentangled representation learning, which casts disentanglement as learning factor-conditioned flows in a compact latent space.
• Score: 48.12507436294143
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Disentangled representation learning aims to capture the underlying explanatory factors of observed data, enabling a principled understanding of the data-generating process. Recent advances in generative modeling have introduced new paradigms for learning such representations. However, existing diffusion-based methods encourage factor independence via inductive biases, yet frequently lack strong semantic alignment. In this work, we propose a flow matching-based framework for disentangled representation learning, which casts disentanglement as learning factor-conditioned flows in a compact latent space. To enforce explicit semantic alignment, we introduce a non-overlap (orthogonality) regularizer that suppresses cross-factor interference and reduces information leakage between factors. Extensive experiments across multiple datasets demonstrate consistent improvements over representative baselines, yielding higher disentanglement scores as well as improved controllability and sample fidelity.

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