@inproceedings{vemuri2026finr,
    type = {inproceedings},
    key = {vemuri2026finr},
    author = {Sai Karthikeya Vemuri and Tim Büchner and Joachim Denzler},
    title = {F-INR: Functional Tensor Decomposition for Implicit Neural Representations},
    year = {2026},
    abstract = {Implicit Neural Representations (INRs) model signals as continuous, differentiable functions. However, monolithic INRs scale poorly with data dimensionality, leading to excessive training costs. We propose F-INR, a framework that addresses this limitation by factorizing a high-dimensional INR into a set of compact, axis-specific sub-networks based on functional tensor decomposition. These sub-networks learn low-dimensional functional components that are then combined via tensor operations. This factorization reduces computational complexity while additionally improving representational capacity. F-INR is both architecture- and decomposition-agnostic. It integrates with various existing INR backbones (e.g., SIREN, WIRE, FINER, Factor Fields) and tensor formats (e.g., CP, TT, Tucker), offering fine-grained control over the speed-accuracy trade-off via the tensor rank and mode. Our experiments show F-INR accelerates training by up to and improves fidelity by over 6.0 dB PSNR compared to state-of-the-art INRs. We validate these gains on diverse tasks, including image representation, 3D geometry reconstruction, and neural radiance fields. We further show F-INR's applicability to scientific computing by modeling complex physics simulations. Thus, F-INR provides a scalable, flexible, and efficient framework for high-dimensional signal modeling.},
    groups = {},
    doi = {10.48550/arXiv.2503.21507},
    booktitle = {Winter Conference on Applications of Computer Vision (WACV)},
    url = {},
    arxiv = {https://arxiv.org/abs/2503.21507},
    code = {},
    note = {(accepted)},
}