A Unified Latent Space Disentanglement VAE Framework with Robust Disentanglement Effectiveness Evaluation

2025-05-02 · Source: stat.ML updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

A new general framework, bfVAE, unifies state-of-the-art disentangled Variational Autoencoders (VAEs) and generates effective latent space disentanglement, particularly for tabular data. To robustly assess VAE disentanglement without requiring ground-truth generative factors, the authors introduce two procedures: Feature Variance Heterogeneity via Latent Traversal (FVH-LT) and Dirty Block Sparse Regression in Latent Space (DBSR-LS). These are complemented by the Latent Space Disentanglement Index (LSDI) for quantitative summary and a greedy alignment strategy (GAS) to mitigate label switching across runs. Extensive experiments on tabular and image data, including FA15, FA24, FA100, white wine, FIFA 2018, CelebA, and MNIST, demonstrate that bfVAE surpasses existing disentangled VAEs in quality and robustness, achieving near-zero false discovery rates for informative latent dimensions. FVH-LT and DBSR-LS reliably uncover semantically meaningful latent structures.

Key takeaway

For Machine Learning Engineers developing VAEs, particularly for real-world tabular data without known generative factors, adopt the bfVAE framework. Its robust disentanglement assessment via FVH-LT and DBSR-LS, combined with LSDI and GAS, provides interpretable latent spaces. This enables more reliable model understanding and guides efficient content generation, reducing reliance on qualitative or supervised methods.

Key insights

Robustly evaluate VAE latent space disentanglement and interpretability without ground-truth generative factors using novel assessment tools.

Principles

• bfVAE unifies state-of-the-art disentangled VAEs, applicable across data modalities.
• Capacity parameter C and $\beta$ control information flow and structure in latent space.
• FVH-LT and DBSR-LS are robust to over-specified latent space dimensionality (K).

Method

The process involves training VAEs multiple times, applying FVH-LT or DBSR-LS to quantify latent-feature associations, using GAS for latent dimension alignment across runs, and summarizing disentanglement effectiveness with LSDI.

In practice

• Use bfVAE with larger C and $\beta < 1$ for tabular data to retain more information.
• Select a slightly larger K for latent space dimensionality when ground-truth K is unknown.
• Apply FVH-LT to identify features relevant for targeted content generation (e.g., wine quality).

Topics

• Variational Autoencoders
• Latent Space Disentanglement
• Tabular Data Analysis
• Image Data Analysis
• Disentanglement Evaluation Metrics
• Information Bottleneck

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by stat.ML updates on arXiv.org.