提出部分解耦VAE，更灵活地处理复杂数据中的语义纠缠。
A Revisit of Total Correlation in Disentangled Variational Auto-Encoder with Partial Disentanglement
- 用局部相关性替代全独立约束，支持分组独立建模。
- 在合成与真实数据上均发现传统方法遗漏的有用信息。
- 适用于解耦程度不一的数据，适合追求实用性的研究者。
完全解耦变分自编码器（VAE）旨在从观测中识别出相互独立的潜在成分。然而，在某些数据集上强制所有潜在成分完全独立可能过于严格。例如，多个因素可能以不可分割的方式纠缠在一起，或单一语义含义可能由高维流形中的多个潜在成分共同表示。为应对这类情况，本文提出部分解耦变分自编码器（PDisVAE），将完全解耦VAE中的总相关性（TC）项推广为局部相关性（PC）项。该框架可处理分组独立性，并自然退化为标准VAE或完全解耦VAE。通过三个合成实验验证了PDisVAE的正确性与实用性。应用于真实数据集时，PDisVAE发现了传统完全解耦VAE难以捕捉的有价值信息，表明其具备良好的通用性与有效性。
原文摘要 · Abstract (English)
A fully disentangled variational auto-encoder (VAE) aims to identify disentangled latent components from observations. However, enforcing full independence between all latent components may be too strict for certain datasets. In some cases, multiple factors may be entangled together in a non-separable manner, or a single independent semantic meaning could be represented by multiple latent components within a higher-dimensional manifold. To address such scenarios with greater flexibility, we develop the Partially Disentangled VAE (PDisVAE), which generalizes the total correlation (TC) term in fully disentangled VAEs to a partial correlation (PC) term. This framework can handle group-wise independence and can naturally reduce to either the standard VAE or the fully disentangled VAE. Validation through three synthetic experiments demonstrates the correctness and practicality of PDisVAE. When applied to real-world datasets, PDisVAE discovers valuable information that is difficult to find using fully disentangled VAEs, implying its versatility and effectiveness.
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