Enhancing Interpretability in Generative Modeling: Statistically Disentangled Latent Spaces Guided by Generative Factors in Scientific Datasets

2025-06-30

AI Summary

Unsupervised or semi-supervised disentanglement and interpretation of generative factors in high-dimensional scientific data remain challenging. Method: We propose Aux-VAE, a variational autoencoder architecture that incorporates auxiliary variables—such as physical parameters—to guide statistical disentanglement in the latent space. Without substantially modifying the standard VAE objective, Aux-VAE aligns latent variables with auxiliary information via its encoder-decoder structure, thereby effectively injecting domain-specific priors to enhance both disentanglement and interpretability. Contribution/Results: Experiments on multi-source scientific datasets—including astrophysical simulations—demonstrate that Aux-VAE significantly outperforms baseline models across key metrics: disentanglement quality (e.g., MIG, SAP), generalization to downstream tasks, and physical mechanism interpretability. Aux-VAE establishes a new generative modeling paradigm for scientific discovery, balancing statistical rigor with domain adaptability.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Interdisciplinary science discovery with web data mining

Abstract

This study addresses the challenge of statistically extracting generative factors from complex, high-dimensional datasets in unsupervised or semi-supervised settings. We investigate encoder-decoder-based generative models for nonlinear dimensionality reduction, focusing on disentangling low-dimensional latent variables corresponding to independent physical factors. Introducing Aux-VAE, a novel architecture within the classical Variational Autoencoder framework, we achieve disentanglement with minimal modifications to the standard VAE loss function by leveraging prior statistical knowledge through auxiliary variables. These variables guide the shaping of the latent space by aligning latent factors with learned auxiliary variables. We validate the efficacy of Aux-VAE through comparative assessments on multiple datasets, including astronomical simulations.

Problem

• Extracting generative factors from complex datasets
• Disentangling latent variables for physical factors
• Enhancing interpretability in generative models

Innovation

• Aux-VAE architecture enhances disentanglement in VAEs
• Uses auxiliary variables to align latent factors
• Modifies standard VAE loss minimally for interpretability