用样条网络提升艺术风格分类，更好捕捉复杂风格特征。
Beyond Linear Bottlenecks: Spline-Based Knowledge Distillation for Culturally Diverse Art Style Classification
- 用KAN替代MLP，通过样条激活建模非线性风格关联
- 在WikiArt和Pandora18k上提升分类准确率，优于基线框架
- 适合研究艺术风格建模与非线性表示学习的学者
艺术风格分类在计算美学中仍具挑战，源于专家标注数据稀缺及风格元素间复杂的非线性交互。现有双教师自监督框架虽减少对标签数据依赖，但其线性投影层和局部关注难以建模全局构图上下文与复杂风格-特征关系。本文通过将传统MLP投影与预测头替换为基于样条的柯尔莫哥洛夫-阿诺德网络（KAN），增强双教师知识蒸馏框架。该方法保留两个教师网络的互补指导：一个聚焦局部纹理与笔触模式，另一个捕捉更广泛风格层级。利用KAN的样条激活，精确建模非线性特征相关性。在WikiArt和Pandora18k数据集上的实验表明，该方法在Top-1准确率上优于基础双教师架构。结果凸显了KAN在解耦复杂风格流形中的重要性，线性探测精度显著高于MLP投影。
原文摘要 · Abstract (English)
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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