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.
66ea3f62fdd4048dceb17f9d
Ali Mehrabian
claimed_verified
2024-10-08T07:22:35.972Z
670498136282fcabb755cd69
/avatars/3c342e5a029636d2f233c28250f6d457.svg
Ali Mehrabian
user
Ali-Meh619
66ea3f62fdd4048dceb17f9e
Parsa Mojarad Adi
66ea3f62fdd4048dceb17f9f
Moein Heidari
admin_assigned
2024-09-18T09:05:54.027Z
61ba19bf6122a4fd29049371
https://cdn-avatars.huggingface.co/v1/production/uploads/1639586194527-noauth.jpeg
Moein Heidari
user
moein99
66ea3f62fdd4048dceb17fa0
Ilker Hacihaliloglu
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.
The proposed Fourier Kolmogorov Arnold network (FKAN) enhances implicit neural representations by using learnable Fourier series activations to better capture frequency-specific components, leading to improved performance in image and 3D occupancy volume tasks.
Implicit neural representations INRs Fourier Kolmogorov Arnold network FKAN Fourier series activation functions peak signal-to-noise ratio PSNR structural similarity index measure SSIM intersection over union IoU