Microstructure Cluster Analysis with Transfer Learning and Unsupervised Learning
Source abstract
Abstract
Abstract We apply computer vision and machine learning methods to analyze two datasets of microstructural images. A transfer learning pipeline utilizes the fully connected layer of a pre-trained convolutional neural network as the image representation. An unsupervised learning method uses the image representations to discover visually distinct clusters of images within two datasets. A minimally supervised clustering approach classifies micrographs into visually similar groups. This approach successfully classifies images both in a dataset of surface defects in steel, where the image classes are visually distinct and in a dataset of fracture surfaces that humans have difficulty classifying. We find that the unsupervised, transfer learning method gives results comparable to fully supervised, custom-built approaches.
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Research context
OpenAlex topics: Image Processing Techniques and Applications · Digital Imaging for Blood Diseases · Domain Adaptation and Few-Shot Learning
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References (28)
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