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Artistic Style Discovery with Independent Components

Style transfer has been well studied in recent years with excellent performance processed. While existing methods usually choose CNNs as the powerful tool to accomplish superb stylization, less attention was paid to the latent style space. Rare exploration of underlying dimensions results in the poor style controllability and the limited practical application. In this work, we rethink the internal meaning of style features, further proposing a novel unsupervised algorithm for style discovery and achieving personalized manip-ulation. In particular, we take a closer look into the mechanism of style transfer and obtain different artistic style components from the latent space consisting of different style features. Then fresh styles can be generated by linear combination according to various style components. Experimental results have shown that our approach is superb in 1) restylizing the original output with the diverse artistic styles discovered from the latent space while keeping the content unchanged, and 2) being generic and compatible for various style transfer methods. Our code is available in this page: https://github.com/Shelsin/ArtIns.