Compact representation and identification of important regions of metal microstructures using complex-step convolutional autoencoders

[Display omitted] •Dual-phase steel microstructural images are compactly represented with a compression ratio of 32.•The trained decoder network of the configured convolutional autoencoder facilitates the secure sharing of microstructural data.•Saliency maps generated in the study highlights the imp...

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Bibliographic Details

Published in:	Materials & design Vol. 223; p. 111236
Main Authors:	Arumugam, Dharanidharan, Kiran, Ravi
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.11.2022 Elsevier
Subjects:	ASTM A992 Convolutional autoencoder Interpretable AI Pixel relevance Saliency maps Saliency maps ASTM A992 Pixel relevance Convolutional autoencoder Interpretable AI
ISSN:	0264-1275, 1873-4197
Online Access:	Get full text
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