用辅助变量让生成模型的隐空间更易解释，物理因素分离更准确
Enhancing Interpretability in Generative Modeling: Statistically Disentangled Latent Spaces Guided by Generative Factors in Scientific Datasets
- 在变分自编码器中引入辅助变量，引导隐空间对齐物理因素
- 在天体模拟等多数据集上实现隐变量解耦，显著提升可解释性
- 适合需要理解生成过程机制的科研人员使用
本研究针对复杂高维科学数据集在无监督或半监督设置下提取生成因素的挑战，探索基于编码器-解码器的生成模型用于非线性降维，重点实现与独立物理因素对应的低维隐变量解耦。提出一种名为Aux-VAE的新架构，在经典变分自编码器框架内仅通过少量修改损失函数，利用辅助变量引入先验统计知识，引导隐空间结构使隐因子与学习到的辅助变量对齐。在多个数据集（包括天体模拟）上的对比实验验证了Aux-VAE的有效性。
原文摘要 · Abstract (English)
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.
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