title: Implicit Neural Representations with Fourier Kolmogorov-Arnold Networks
summary: 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.
paper_authors._id: 66ea3f62fdd4048dceb17f9d
paper_authors.name: Ali Mehrabian
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-08T07:22:35.972Z
paper_authors._id: 670498136282fcabb755cd69
paper_authors.avatarUrl: /avatars/3c342e5a029636d2f233c28250f6d457.svg
paper_authors.fullname: Ali Mehrabian
paper_authors.type: user
paper_authors.user: Ali-Meh619
paper_authors._id: 66ea3f62fdd4048dceb17f9e
paper_authors.name: Parsa Mojarad Adi
paper_authors._id: 66ea3f62fdd4048dceb17f9f
paper_authors.name: Moein Heidari
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-18T09:05:54.027Z
paper_authors._id: 61ba19bf6122a4fd29049371
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1639586194527-noauth.jpeg
paper_authors.fullname: Moein Heidari
paper_authors.type: user
paper_authors.user: moein99
paper_authors._id: 66ea3f62fdd4048dceb17fa0
paper_authors.name: Ilker Hacihaliloglu
paper_title: Implicit Neural Representations with Fourier Kolmogorov-Arnold Networks
paper_summary: 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.
paper_ai_keywords: Implicit neural representations (INRs) Fourier Kolmogorov Arnold network (FKAN) learnable activation functions Fourier series task-specific frequency components high-resolution high-dimensional data peak signal-to-noise ratio (PSNR) structural similarity index measure (SSIM) intersection over union (IoU) 3D occupancy volume representation