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Ede, Jeffrey M. (2020) Warwick electron microscopy Datasets. Machine Learning : Science and Technology, 1 (4). 045003. doi:10.1088/2632-2153/ab9c3c

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Official URL: https://doi.org/10.1088/2632-2153/ab9c3c

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Abstract

Large, carefully partitioned datasets are essential to train neural networks and standardize performance benchmarks. As a result, we have set up new repositories to make our electron microscopy datasets available to the wider community. There are three main datasets containing 19769 scanning transmission electron micrographs, 17266 transmission electron micrographs, and 98340 simulated exit wavefunctions, and multiple variants of each dataset for different applications. To visualize image datasets, we trained variational autoencoders to encode data as 64-dimensional multivariate normal distributions, which we cluster in two dimensions by t-distributed stochastic neighbor embedding. In addition, we have improved dataset visualization with variational autoencoders by introducing encoding normalization and regularization, adding an image gradient loss, and extending t-distributed stochastic neighbor embedding to account for encoded standard deviations. Our datasets, source code, pretrained models, and interactive visualizations are openly available at https://github.com/Jeffrey-Ede/datasets.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Q Science > QC Physics
Z Bibliography. Library Science. Information Resources > ZA Information resources
Divisions: Faculty of Science > Physics
Library of Congress Subject Headings (LCSH): Data sets , Electron microscopy, Machine learning , Document markup languages
Journal or Publication Title: Machine Learning : Science and Technology
Publisher: IOP Publishing Ltd
ISSN: 2632-2153
Official Date: 11 September 2020
Dates:
DateEvent
11 September 2020Published
12 June 2020Accepted
Volume: 1
Number: 4
Article Number: 045003
DOI: 10.1088/2632-2153/ab9c3c
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access
Copyright Holders: © 2020 The Author(s). Published by IOP Publishing Ltd
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
EP/N035437/1[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
Studentship 1917382[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
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