Microstructure synthesis using style-based generative adversarial networks.

Daria Fokina, Ekaterina Muravleva, George Ovchinnikov, Ivan Oseledets
Author Information
  1. Daria Fokina: Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30, bld. 1, 143025, Moscow, Russia.
  2. Ekaterina Muravleva: Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30, bld. 1, 143025, Moscow, Russia.
  3. George Ovchinnikov: Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30, bld. 1, 143025, Moscow, Russia.
  4. Ivan Oseledets: Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30, bld. 1, 143025, Moscow, Russia.

Abstract

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.

Word Cloud

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