Maciej Pęśko and Tomasz Trzciński’s Algorithm can Produce an Image in the Style of Another Image


From MIT Technology Review:

Enter Pęśko and Trzciński. These guys have tested a wide range of neural style transfer algorithms on the specific task of transferring the graphic styles associated with comics. “This is the first attempt to evaluate and compare the results obtained by several methods in the context of transferring comic style,” they say.

They specifically focus on the fastest techniques that have the potential to work on any graphic image. “We focus mostly on methods whose execution time per image do not exceed 2 seconds,” they say.

In this way, they tested five different algorithms on 600×450-pixel images processed using a 12-gigabyte Titan X graphics processing unit. They selected images that represent various comic styles and transferred these to images chosen randomly from a Google image search.

Finally, they showed the results to 100 people to evaluate how well the algorithms achieved the style transfer.

The results show the state of the art in this area. The algorithm judged best is a technique known as adaptive instance normalization, developed in 2017, with some 30 percent of the votes in its favor. “It confirms our assumptions that this method gives results that are the closest to cartoon or comics in terms of stylistic similarity,” says Pęśko and Trzciński.

Read more from MIT Technology Review and Cornell University Library

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