Implicit Neural Representations with Fourier Kolmogorov-Arnold Networks
<|reference_start|>Implicit Neural Representations with Fourier Kolmogorov-Arnold Networks: Implicit neural representations (INRs) use neural networks to provide continuous and resolution-independent representations of complex signals with a small number of parameters. However, existing INR models often fail to capture important frequency components specific to each task. To address this issue, in this paper, we propose a Fourier Kolmogorov Arnold network (FKAN) for INRs. The proposed FKAN utilizes learnable activation functions modeled as Fourier series in the first layer to effectively control and learn the task-specific frequency components. In addition, the activation functions with learnable Fourier coefficients improve the ability of the network to capture complex patterns and details, which is beneficial for high-resolution and high-dimensional data. Experimental results show that our proposed FKAN model outperforms three state-of-the-art baseline schemes, and improves the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) for the image representation task and intersection over union (IoU) for the 3D occupancy volume representation task, respectively.<|reference_end|>
@article{mehrabian2024implicit,
    title={Implicit Neural Representations with Fourier Kolmogorov-Arnold Networks},
    author={Ali Mehrabian, Parsa Mojarad Adi, Moein Heidari, Ilker Hacihaliloglu},
    journal={arXiv preprint arXiv:2409.09323},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.09323},
    primaryClass={cs.CV}
}