用奖励机制自动寻找显微图像的最佳简化表示
Reward driven discovery of the optimal microstructure representations with invariant variational autoencoders
- 用奖励函数评估变分自编码器的潜在空间表现
- 高斯混合模型能有效估计模型效率并指导搜索
- 适合材料科学与复杂图像数据简化研究者
显微技术生成海量复杂图像数据，理论上可用于发现更简单、可解释且简洁的表征形式，以揭示分子系统中的基本单元或晶体材料中的序参量与相态。变分自编码器（VAEs）是构建低维表征的强大工具，但其性能严重依赖多个非短期设计选择，通常需通过试错和经验分析优化。为实现VAE工作流的自动化与无偏优化，我们研究了基于奖励的策略来评估潜在空间表示。以压电力原子力显微镜（Piezoresponse Force Microscopy）数据为模型系统，考察了多种策略与奖励函数，可作为自动化优化的基础。分析表明，使用高斯混合模型（GMM）和贝叶斯高斯混合模型（BGMM）近似潜在空间，能为构建奖励函数提供坚实基础，从而估计模型效率并引导寻找最优简约表征。
原文摘要 · Abstract (English)
Microscopy techniques generate vast amounts of complex image data that in principle can be used to discover simpler, interpretable, and parsimonious forms to reveal the underlying physical structures, such as elementary building blocks in molecular systems or order parameters and phases in crystalline materials. Variational Autoencoders (VAEs) provide a powerful means of constructing such low-dimensional representations, but their performance heavily depends on multiple non-myopic design choices, which are often optimized through trial-and-error and empirical analysis. To enable automated and unbiased optimization of VAE workflows, we investigated reward-based strategies for evaluating latent space representations. Using Piezoresponse Force Microscopy data as a model system, we examined multiple policies and reward functions that can serve as a foundation for automated optimization. Our analysis shows that approximating the latent space with Gaussian Mixture Models (GMM) and Bayesian Gaussian Mixture Models (BGMM) provides a strong basis for constructing reward functions capable of estimating model efficiency and guiding the search for optimal parsimonious representations.
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