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4D Holography of Beam/topological Defect Interaction and Big, Smart, and Deep Data

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4D Holography of Beam/topological Defect Interaction and Big, Smart, and Deep Data
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16
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CC Attribution - NonCommercial - NoDerivatives 4.0 International:
You are free to use, copy, distribute and transmit the work or content in unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
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Abstract
Deep learning introduces the potential for autonomous S/TEM characterization, a step towards unsupervised data acquisition and analysis through machine learning. Whole image classification is the first step towards the long-term goal of autonomous image data acquisition and analysis. We have successfully retrained AlexNet, b-FCN, and U-Net, pre-existing deep learning Convolutional Neural Networks(CNNs), for autonomous, whole image classification and analysis, on TEM image datasets.
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