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Synthetic Data Curation Strategies for Robust Model Development: A Case Study with HRTEM Micrograph Segmentation

Published online by Cambridge University Press:  22 July 2022

Luis Rangel DaCosta ,
Katherine Sytwu ,
Catherine Groschner  and
Mary Scott
Luis Rangel DaCosta*
Affiliation:
University of California, Berkeley, Department of Materials Science, Berkeley, CA National Center for Electron Microscopy, Lawrence Berkeley National Laboratory, Berkeley, CA
Katherine Sytwu
Affiliation:
National Center for Electron Microscopy, Lawrence Berkeley National Laboratory, Berkeley, CA
Catherine Groschner
Affiliation:
University of California, Berkeley, Department of Materials Science, Berkeley, CA National Center for Electron Microscopy, Lawrence Berkeley National Laboratory, Berkeley, CA
Mary Scott
Affiliation:
University of California, Berkeley, Department of Materials Science, Berkeley, CA National Center for Electron Microscopy, Lawrence Berkeley National Laboratory, Berkeley, CA
*
*[email protected]

Abstract

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Type
Artificial Intelligence, Instrument Automation, And High-dimensional Data Analytics for Microscopy and Microanalysis
Information
Microscopy and Microanalysis , Volume 28 , Supplement S1 , August 2022 , pp. 3098 - 3100
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Copyright
Copyright © Microscopy Society of America 2022

References

Ede, J. M., Mach. Learn.: Sci. Technol. 2, (2021), p. 011004. doi: 10.1088/2632-2153/abd614.Google Scholar
Munshi, J. et al. , arXiv:2202.00204, (2022). doi: 10.48550/arXiv.2202.00204CrossRefGoogle Scholar
Ong, S. P. et al. , Computational Materials Science, 68 (2013), p. 314–319. doi: 10.1016/j.commatsci.2012.10.028.CrossRefGoogle Scholar
Larsen, A. H. et al. , J. Phys.: Condens. Matter, 29 (2017), doi: 10.1088/1361-648X/aa680e.Google Scholar
Rahm, J. M. and Erhart, P., Journal of Open Source Software, 5 (2020), p. 1944. doi: 10.21105/joss.01944.CrossRefGoogle Scholar
Rangel DaCosta, L. et al. , Micron, 151 (2021), p. 103141. doi: 10.1016/j.micron.2021.103141.CrossRefGoogle Scholar