KD-INR : Time-Varying Volumetric Data Compression via Knowledge Distillation-Based Implicit Neural Representation
Traditional deep learning algorithms assume that all data is available during training, which presents challenges when handling large-scale time-varying data. To address this issue, we propose a data reduction pipeline called knowledge distillation-based implicit neural representation (KD-INR) for c...
| Veröffentlicht in: | IEEE transactions on visualization and computer graphics. - 1996. - 30(2024), 10 vom: 21. Sept., Seite 6826-6838 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
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2024
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on visualization and computer graphics |
| Schlagworte: | Journal Article |
| Online verfügbar |
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