Artist Style Transfer Via Quadratic Potential
This work addresses the problem of generating artist-stylized images for applications in digital art and media, but it appears incremental as it builds on existing methods with a new divergence metric.
The paper tackles artist style transfer by applying a painting style to photographs using neural networks trained with quadratic potential divergence for stability, and integrates recent deep learning techniques to improve image quality.
In this paper we address the problem of artist style transfer where the painting style of a given artist is applied on a real world photograph. We train our neural networks in adversarial setting via recently introduced quadratic potential divergence for stable learning process. To further improve the quality of generated artist stylized images we also integrate some of the recently introduced deep learning techniques in our method. To our best knowledge this is the first attempt towards artist style transfer via quadratic potential divergence. We provide some stylized image samples in the supplementary material. The source code for experimentation was written in PyTorch and is available online in my GitHub repository.