Beyond Linear Bottlenecks: Spline-Based Knowledge Distillation for
  Culturally Diverse Art Style Classification
Authors: name: Abdellah Zakaria Sellam, name: Salah Eddine Bekhouche, name: Cosimo Distante, name: Abdelmalik Taleb-Ahmed

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.