Microstructure synthesis using style-based generative adversarial networks

Daria Fokina, Ekaterina Muravleva, George Ovchinnikov, Ivan Oseledets

    Research output: Contribution to journalArticlepeer-review

    23 Citations (Scopus)


    This work considers the usage of StyleGAN architecture for the task of microstructure synthesis. The task is the following: Given number of samples of structure we try to generate similar samples at the same time preserving its properties. Since the considered architecture is not able to produce samples of sizes larger than the training images, we propose to use image quilting to merge fixed-sized samples. One of the key features of the considered architecture is that it uses multiple image resolutions. We also investigate the necessity of such an approach.

    Original languageEnglish
    Article number043308
    JournalPhysical Review E - Statistical, Nonlinear, and Soft Matter Physics
    Issue number4
    Publication statusPublished - Apr 2020


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