The Dawn of KAN in Image-to-Image (I2I) Translation: Integrating Kolmogorov-Arnold Networks with GANs for Unpaired I2I Translation

TL;DR

This paper introduces KAN-CUT, a novel image-to-image translation model that replaces MLP with Kolmogorov-Arnold Networks, resulting in improved feature representation and higher quality image generation in unpaired translation tasks.

Contribution

The study demonstrates that replacing MLP with KAN in contrastive unpaired image translation enhances generative quality and informative feature extraction, advancing the capabilities of GAN-based models.

Findings

KAN improves feature informativeness in low-dimensional vectors

KAN-CUT outperforms traditional MLP-based models in image quality

Extensive experiments validate KAN's effectiveness in unpaired I2I translation

Abstract

Image-to-Image translation in Generative Artificial Intelligence (Generative AI) has been a central focus of research, with applications spanning healthcare, remote sensing, physics, chemistry, photography, and more. Among the numerous methodologies, Generative Adversarial Networks (GANs) with contrastive learning have been particularly successful. This study aims to demonstrate that the Kolmogorov-Arnold Network (KAN) can effectively replace the Multi-layer Perceptron (MLP) method in generative AI, particularly in the subdomain of image-to-image translation, to achieve better generative quality. Our novel approach replaces the two-layer MLP with a two-layer KAN in the existing Contrastive Unpaired Image-to-Image Translation (CUT) model, developing the KAN-CUT model. This substitution favors the generation of more informative features in low-dimensional vector representations, which contrastive learning can utilize more effectively to produce high-quality images in the target domain. Extensive experiments, detailed in the results section, demonstrate the applicability of KAN in conjunction with contrastive learning and GANs in Generative AI, particularly for image-to-image translation. This work suggests that KAN could be a valuable component in the broader generative AI domain.