标题： 增强生成建模中的可解释性：由科学数据集中的生成因素引导的统计解耦潜在空间 显示英文标题

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

摘要： 本研究解决了在无监督或半监督设置中从复杂高维数据集中统计提取生成因素的挑战。 我们研究了基于编码器-解码器的生成模型，用于非线性降维，重点在于分离与独立物理因素对应的低维潜在变量。 引入 Aux-VAE，这是一种经典的变分自动编码器框架内的新架构，通过借助辅助变量的先验统计知识，仅对标准 VAE 损失函数进行最小修改，实现了分离。 这些变量通过将潜在因子与学习到的辅助变量对齐，指导潜在空间的形成。 我们通过在多个数据集（包括天文学模拟）上的比较评估验证了 Aux-VAE 的有效性。 显示英文摘要

摘要： 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.