About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Presentation Title |
Addressing Annotated Data Scarcity and Materials Diversity with Advanced Deep Learning Architectures |
Author(s) |
Ali Riza Durmaz, Aurčle Goetz, Edward Kreutzarek, Martin Müller, Chris Eberl |
On-Site Speaker (Planned) |
Ali Riza Durmaz |
Abstract Scope |
Materials' microstructures are the footprint of the entire processing route rendering them very diverse. As materials discovery is accelerated and materials become increasingly intricate, deep learning (DL) gains in significance for many tasks. Especially when domain knowledge is incomplete, DL frequently outperforms classical techniques by a large margin. However, its poor data-efficiency (1) and inter-domain generalizability (2) are inherently conflicting with the expense of data annotation and materials diversity, respectively.
To alleviate both issues, we propose to apply two advanced computer vision architectures that are trained in partially supervised fashion, i.e., additionally exploiting unannotated data. Specifically, we utilize (1) semi-supervised learning using dynamic mutual training and (2) unsupervised domain adaptation (transfer across materials domains) to address both challenges. Both frameworks are investigated on a complex phase steel segmentation task utilizing micrographs with different surface etchings and imaging modalities. These state-of-the-art frameworks outperform conventional supervised training strategies and pre-trained networks. |
Proceedings Inclusion? |
Undecided |