Hyb-KAN ViT: Hybrid Kolmogorov-Arnold Networks Augmented Vision Transformer (2505.04740v1)

Abstract: This study addresses the inherent limitations of Multi-Layer Perceptrons (MLPs) in Vision Transformers (ViTs) by introducing Hybrid Kolmogorov-Arnold Network (KAN)-ViT (Hyb-KAN ViT), a novel framework that integrates wavelet-based spectral decomposition and spline-optimized activation functions, prior work has failed to focus on the prebuilt modularity of the ViT architecture and integration of edge detection capabilities of Wavelet functions. We propose two key modules: Efficient-KAN (Eff-KAN), which replaces MLP layers with spline functions and Wavelet-KAN (Wav-KAN), leveraging orthogonal wavelet transforms for multi-resolution feature extraction. These modules are systematically integrated in ViT encoder layers and classification heads to enhance spatial-frequency modeling while mitigating computational bottlenecks. Experiments on ImageNet-1K (Image Recognition), COCO (Object Detection and Instance Segmentation), and ADE20K (Semantic Segmentation) demonstrate state-of-the-art performance with Hyb-KAN ViT. Ablation studies validate the efficacy of wavelet-driven spectral priors in segmentation and spline-based efficiency in detection tasks. The framework establishes a new paradigm for balancing parameter efficiency and multi-scale representation in vision architectures.

Hybrid Kolmogorov-Arnold Networks Augmented Vision Transformer: An Overview

The presented research explores the augmentation of Vision Transformers (ViTs) with hybrid Kolmogorov-Arnold Networks, proposing the Hyb-KAN ViT as an innovative framework poised to address the inherent limitations of Multi-Layer Perceptrons (MLPs) in conventional ViT architectures. This approach synergistically integrates wavelet-based spectral decomposition and spline-optimized activation functions, redefining how vision architectures balance parameter efficiency with the ability to capture multi-scale representations.

Methodological Innovations

The paper introduces two fundamental modules within the Hyb-KAN ViT architecture:

1. Efficient-KAN (Eff-KAN): This module innovatively replaces the traditional MLP layers with spline functions. Spline-based activations offer smoother decision boundaries, maintaining the ability to model complex data patterns and improving computational efficiency.
2. Wavelet-KAN (Wav-KAN): Leveraging orthogonal wavelet transforms, Wav-KAN facilitates multi-resolution feature extraction, capturing both high-frequency and low-frequency image components—an essential requirement for robust feature extraction in vision tasks.

Strategically incorporated into ViT encoder layers and classification heads, these modules enhance the spatial-frequency modeling capabilities while alleviating computational bottlenecks. The proposed framework demonstrates superior performance on prominent datasets such as ImageNet-1K, COCO, and ADE20K, marking a state-of-the-art advancement in tasks like image recognition, object detection, and semantic segmentation.

Experimental Findings

Comprehensive experiments revealed:

• On ImageNet-1K, Wav-KAN ViTs, particularly with the Derivative of Gaussian (DoG) wavelet, outperformed baseline Eff-KAN and original ViTs in Top-1 accuracy.
• Semantic segmentation tasks using ADE20K benefited significantly from the spectral frequency-driven capabilities of Wav-KAN.
• Ablation studies validated the effectiveness of wavelet-driven spectral priors in improving segmentation and the efficiency of spline-based activations in detection tasks.

Hyb-KAN ViT particularly excelled, achieving enhanced hierarchical feature extraction by merging wavelet-driven analytical prowess with spline-based processing efficiency.

Implications and Future Directions

The implications of Hyb-KAN ViT extend beyond immediate performance improvements. The integration of wavelet and spline methods within transformer architectures signifies a promising shift towards more parameter-efficient models capable of handling diverse vision tasks. However, the paper also highlights computational challenges tied to scaling, particularly regarding Eff-KAN's quadratic complexity.

Future work could explore advancements in attention mechanisms and further optimization of KAN-related architectures. Specifically, efforts could focus on reducing parameter overhead through techniques like parameter multiplexing, as suggested by the reference to GR-KAN, potentially leading to a substantial decrease in parameter count while maintaining representational integrity.

In sum, the Hyb-KAN ViT framework stands as a significant contribution to the evolution of vision transformers, demonstrating that thoughtfully designed hybrid models can yield superior performance through enhanced multi-scale feature representation without succumbing to the computational inefficiencies that traditionally accompany such advancements in neural architecture design.