KAN在图像到图像（I2I）翻译领域的曙光：将柯尔莫哥洛夫-阿诺德网络与GANs融合用于无配对I2I翻译
The Dawn of KAN in Image-to-Image (I2I) Translation: Integrating Kolmogorov-Arnold Networks with GANs for Unpaired I2I Translation
AI总结：
本研究提出KAN-CUT模型，用两层KAN替换CUT模型中的两层MLP，验证了KAN结合对比学习与GANs在无配对图像到图像翻译任务中能提升生成质量，拓展了KAN在生成式AI领域的应用可能。
摘要：
生成式人工智能（Generative AI）中的图像到图像翻译一直是研究核心焦点，其应用覆盖医疗保健、遥感、物理、化学、摄影等诸多领域。在众多方法中，结合对比学习的生成对抗网络（GANs）取得了尤为突出的成效。本研究旨在证明柯尔莫哥洛夫-阿诺德网络（KAN）能够有效替代生成式AI中的多层感知机（MLP）方法，尤其是在图像到图像翻译子领域，以实现更优的生成质量。我们提出的新方法在现有的对比无配对图像到图像翻译（CUT）模型中，用两层KAN替换了原有的两层MLP，构建出KAN-CUT模型。这种替换有助于在低维向量表示中生成信息量更丰富的特征，对比学习可以更高效地利用这些特征，生成目标域的高质量图像。结果部分详述的大量实验证明，KAN与对比学习、GANs结合在生成式AI中具备适用性，尤其适用于图像到图像翻译任务。这项工作表明，KAN有望成为更广泛的生成式AI领域中极具价值的组成部分。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
