论文标题
来自生成模型的合成材料微结构图像的评估指标的调查
A Survey on Evaluation Metrics for Synthetic Material Micro-Structure Images from Generative Models
论文作者
论文摘要
随着机器学习和材料科学研究的发展，合成微结构图像的评估是一个新的问题。评估生成模型的合成图像的最典型状态取决于Fréchet的启动距离。但是，由于特征在物理准确的微观结构和有限的数据集大小的独特功能上，这种和其他类似的方法在材料域中受到限制。在这项研究中，我们评估了用于扫描电子显微镜（SEM）图像石墨烯富含聚氨酯泡沫的各种方法。本文的主要目的是报告我们关于现有方法缺点的发现，以鼓励机器学习社区考虑指标的增强，以评估材料科学领域中合成图像的质量。
The evaluation of synthetic micro-structure images is an emerging problem as machine learning and materials science research have evolved together. Typical state of the art methods in evaluating synthetic images from generative models have relied on the Fréchet Inception Distance. However, this and other similar methods, are limited in the materials domain due to both the unique features that characterize physically accurate micro-structures and limited dataset sizes. In this study we evaluate a variety of methods on scanning electron microscope (SEM) images of graphene-reinforced polyurethane foams. The primary objective of this paper is to report our findings with regards to the shortcomings of existing methods so as to encourage the machine learning community to consider enhancements in metrics for assessing quality of synthetic images in the material science domain.
