The paper "A Learned Representation for Artistic Style" by Vincent Dumoulin, Jonathon Shlens, and Manjunath Kudlur explores the construction of a scalable deep network that can capture the artistic styles of diverse paintings. The authors introduce a method called conditional instance normalization, which allows a single network to learn multiple styles simultaneously. This approach reduces the number of parameters required for style transfer, making it more efficient and practical for mobile applications. The network is trained to reduce a painting to a point in an embedding space, enabling users to explore new styles by combining learned styles from individual paintings. The paper demonstrates that the learned representation is flexible and can capture a wide range of artistic styles, including those with significant variations in color palette and spatial scale. The authors also show that the network generalizes well across different painting styles and can produce high-quality pastiches with minimal impact on training time and performance. The learned style representation is useful for arbitrary combinations of artistic styles, suggesting the existence of a rich and flexible learned representation for artistic styles.The paper "A Learned Representation for Artistic Style" by Vincent Dumoulin, Jonathon Shlens, and Manjunath Kudlur explores the construction of a scalable deep network that can capture the artistic styles of diverse paintings. The authors introduce a method called conditional instance normalization, which allows a single network to learn multiple styles simultaneously. This approach reduces the number of parameters required for style transfer, making it more efficient and practical for mobile applications. The network is trained to reduce a painting to a point in an embedding space, enabling users to explore new styles by combining learned styles from individual paintings. The paper demonstrates that the learned representation is flexible and can capture a wide range of artistic styles, including those with significant variations in color palette and spatial scale. The authors also show that the network generalizes well across different painting styles and can produce high-quality pastiches with minimal impact on training time and performance. The learned style representation is useful for arbitrary combinations of artistic styles, suggesting the existence of a rich and flexible learned representation for artistic styles.