| Abstrakt: |
To translate a real-world photograph into an artistic image in the style of a famous artist, the selection of colors and brushstrokes should reflect those of the artist. A one-to-one domain translation architecture, CycleGAN, trained with an unpaired dataset can be used to translate a real-world photograph into an artistic image. However, to translate images in Nnumber of multi-artistic styles, the disadvantage is that more than one CycleGAN must be trained corresponding to each style. Here, we develop a single deep learning architecture that can be controlled to yield multiple artistic styles by adding a conditional vector. The overall architecture includes a one-to-Ndomain translation architecture, namely, a conditional CycleGAN, and an N-to-Ndomain translation architecture, namely, StarGAN, for translating into five different artistic styles. An evaluation of the trained models reveal that multiple artistic styles can be produced from a single real-world photograph only by adjusting the conditional input. |
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