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The aim is to develop a framework that can classify artistic paintings by separating sub-styles effectively.

A Self-supervised Sub-style Separation Framework for Artistic Painting Classification

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The aim is to develop a framework that can classify artistic paintings by separating sub-styles effectively.
Developed a self-supervised learning framework for art classification.
Implemented techniques to identify and separate sub-styles of paintings.
Utilized various datasets of artistic paintings for training and testing.
Achieved improved accuracy in classifying paintings based on sub-styles.
Demonstrated robustness across different artistic genres and styles.

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A framework demonstrates effective classification of artistic paintings using a self-supervised approach, indicating potential in art analysis.

Huang et al. (2026) studied this question.

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A framework demonstrates effective classification of artistic paintings using a self-supervised approach, indicating potential in art analysis.