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
claimed_verified
2025-08-06T19:29:30.799Z
63e7493fdb40d9e67feaccdd
https://cdn-avatars.huggingface.co/v1/production/uploads/63e7493fdb40d9e67feaccdd/9d1iTreC4GlxSiWvLT0dP.jpeg
Salah Eddine Bekhouche
user
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