mlsanityData to 7 Oct 2026

A Survey on Evaluation Metrics for Synthetic Material Micro-Structure Images from Generative Models

arXiv 2211.09727 ↗First posted 3 Nov 2022cond-mat.mtrl-sci cs.CV cs.LGcited by 1

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Abstract (arXiv)

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 Fr'echet 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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 15 of its 25 references that are in the map. First posted on arXiv 3 Nov 2022.

Builds on

15 of 25 references.

References · 15

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3 more
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Citing papers per year: 2023: 1.

1 paper.

  1. 2023Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Designcited by 22