Abstract
In recent times, artificial intelligence has become more sophisticated when it comes to the creation of fine arts. Especially in the area of painting, artificial methods reached a new level of maturity in the process of replicating perceptual quality. These systems are able to separate style and content of given images, enabling them to recombine and mutate the facets to create novel content. This work defines a general framework for conducting artistic style transfer. This allows recombination and structured modification of state of the art algorithms for further investigation and profiling of artistic style transfer.
Cite
CITATION STYLE
Uhde, F., & Mostaghim, S. (2018). Towards a general framework for artistic style transfer. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10783 LNCS, pp. 177–193). Springer Verlag. https://doi.org/10.1007/978-3-319-77583-8_12
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