基于泰勒展开的Kolmogorov-Arnold网络用于盲图像质量评估
Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment
AI总结：
针对KAN在盲图像质量评估中处理高维特征时性能受限且计算成本高的问题，提出TaylorKAN，利用泰勒展开作为可学习激活函数增强局部逼近能力，并结合网络深度缩减和特征维度压缩提升计算效率，在五个数据库上表现优于其他KAN模型。
摘要：
Kolmogorov-Arnold Network (KAN) 因其强大的函数逼近能力而引起了越来越多的关注。在我们之前的工作中，KAN及其变体被探索用于盲图像质量评估 (BIQA) 的分数回归。然而，这些模型在处理高维特征时遇到了挑战，导致性能提升有限且计算成本增加。为了解决这些问题，我们提出了TaylorKAN，它利用泰勒展开作为可学习的激活函数来增强局部逼近能力。为了提高计算效率，网络深度缩减和特征维度压缩被集成到基于TaylorKAN的分数回归流程中。在五个具有真实失真的数据库 (BID, CLIVE, KonIQ, SPAQ和FLIVE) 上，大量实验表明，TaylorKAN始终优于其他与KAN相关的模型，这表明通过泰勒展开进行的局部逼近比使用正交函数的全局逼近更有效。其泛化能力通过跨数据库实验得到了验证。研究结果突出了TaylorKAN作为用于高维分数回归的高效且鲁棒模型的潜力。
英文摘要：
Kolmogorov-Arnold Network (KAN) has attracted growing interest for its strong function approximation capability. In our previous work, KAN and its variants were explored in score regression for blind image quality assessment (BIQA). However, these models encounter challenges when processing high-dimensional features, leading to limited performance gains and increased computational cost. To address these issues, we propose TaylorKAN that leverages the Taylor expansions as learnable activation functions to enhance local approximation capability. To improve the computational efficiency, network depth reduction and feature dimensionality compression are integrated into the TaylorKAN-based score regression pipeline. On five databases (BID, CLIVE, KonIQ, SPAQ, and FLIVE) with authentic distortions, extensive experiments demonstrate that TaylorKAN consistently outperforms the other KAN-related models, indicating that the local approximation via Taylor expansions is more effective than global approximation using orthogonal functions. Its generalization capacity is validated through inter-database experiments. The findings highlight the potential of TaylorKAN as an efficient and robust model for high-dimensional score regression.
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