超越线性瓶颈：面向文化多元艺术风格分类的基于样条的知识蒸馏
Beyond Linear Bottlenecks: Spline-Based Knowledge Distillation for Culturally Diverse Art Style Classification
AI总结：
针对艺术风格分类中线性投影难建模非线性风格关联的问题，提出用KAN替换双教师自监督知识蒸馏框架的MLP头，在WikiArt和Pandora18k上取得更优Top-1准确率与线性探测效果。
摘要：
在计算美学领域，由于专业标注数据集稀缺，且风格元素之间存在复杂且通常为非线性的相互作用，艺术风格分类仍是一项艰巨挑战。近期的双教师自监督框架虽降低了对标注数据的依赖，但其线性投影层和局部聚焦特性难以建模全局构图语境与复杂的风格-特征相互作用。我们对双教师知识蒸馏框架进行改进以解决这些局限：用Kolmogorov-Arnold Networks（KAN，柯尔莫哥洛夫-阿诺德网络）替换传统的MLP投影头与预测头。我们的方法保留了两个教师网络的互补指导——一个侧重局部纹理与笔触模式，另一个捕捉更宽泛的风格层级结构，同时利用KAN的基于样条的激活函数以数学精度建模非线性特征关联。在WikiArt和Pandora18k数据集上的实验表明，我们的方法在Top-1准确率上优于基础双教师架构。我们的研究凸显了KAN在解耦复杂风格流形方面的重要性，其线性探测准确率优于MLP投影。
英文摘要：
Art style classification remains a formidable challenge in computational aesthetics due to the scarcity of expertly labeled datasets and the intricate, often nonlinear interplay of stylistic elements. While recent dual-teacher self-supervised frameworks reduce reliance on labeled data, their linear projection layers and localized focus struggle to model global compositional context and complex style-feature interactions. We enhance the dual-teacher knowledge distillation framework to address these limitations by replacing conventional MLP projection and prediction heads with Kolmogorov-Arnold Networks (KANs). Our approach retains complementary guidance from two teacher networks, one emphasizing localized texture and brushstroke patterns, the other capturing broader stylistic hierarchies while leveraging KANs' spline-based activations to model nonlinear feature correlations with mathematical precision. Experiments on WikiArt and Pandora18k demonstrate that our approach outperforms the base dual teacher architecture in Top-1 accuracy. Our findings highlight the importance of KANs in disentangling complex style manifolds, leading to better linear probe accuracy than MLP projections.
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