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Latent Space Exploration of Neural Networks for Annotation Efficient Subsurface Interpretation and Characterization
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Title: Latent Space Exploration of Neural Networks for Annotation Efficient Subsurface Interpretation and Characterization
Committee:
Dr. AlRegib, Advisor
Dr. Anderson, Chair
Dr. Davenport
Abstract: The objective of the proposed research is to explore the use of deep autoencoders and learn data manifolds to rank and characterize unlabeled training examples by their outof-distribution-score. It is theorized that this ranking can be used to select and annotate only the most informative training samples for seismic interpretation tasks, greatly reducing the annotation effort for seismic interpreters.
Status
- Workflow Status:Published
- Created By:Daniela Staiculescu
- Created:04/09/2022
- Modified By:Daniela Staiculescu
- Modified:04/09/2022
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