Beyond Linear Bottlenecks: Spline-Based Knowledge Distillation for
  Culturally Diverse Art Style Classification
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.
688c61208c434640078cc4a7
Abdellah Zakaria Sellam
688c61208c434640078cc4a8
Salah Eddine Bekhouche
688c61208c434640078cc4a9
Cosimo Distante
688c61208c434640078cc4aa
Abdelmalik Taleb-Ahmed
Beyond Linear Bottlenecks: Spline-Based Knowledge Distillation for
  Culturally Diverse Art Style Classification
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.
Enhancing dual-teacher self-supervised frameworks with Kolmogorov-Arnold Networks improves art style classification by better modeling nonlinear feature correlations and disentangling complex style manifolds.
dual-teacher self-supervised frameworks Kolmogorov-Arnold Networks MLP projection prediction heads spline-based activations nonlinear feature correlations style manifolds linear probe accuracy