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Arxiv
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F-INR: Functional Tensor Decomposition for Implicit Neural Representations
- F-INR is a framework that reformulates Implicit Neural Representation (INR) learning through functional tensor decomposition.
- It breaks down high-dimensional tasks into lightweight, axis-specific sub-networks, reducing computational costs.
- F-INR is modular, compatible with various INR architectures, and supports different decomposition modes.
- In experiments, F-INR trains 100 times faster than existing approaches while achieving higher fidelity in various tasks.
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