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Die Artistic Style-Transfer-Algorithmen werden immer besser, das psychedelic Kitten und die Matrix-Cat unten rechts ist tatsächlich ziemlich mindblowing. Die Pics stammen aus einem neuen Paper von Roman Novak und Yaroslav Nikulin: Improving the Neural Algorithm of Artistic Style.
Last year, with Deep Dream (June 2015) and Style Networks (August 2015), the idea that deep learning may become a tool for Art entered the public consciousness. Generative algorithms based on Neural Networks so far haven’t been the most predictable or easiest to understand, but when they work — by combination of skill or luck — the quality of the output is second to none!
It took until 2016 for those techniques to be turned into tools that are useful for artists, starting with this paper we call Neural Patches (January 2016) that lets the algorithm process images in a context-sensitive manner. Now, when style transfer techniques are extended with controls and annotations, they can process images in a meaningful way: reducing glitches and increasing user control. This is our work on Semantic Style Transfer (March 2016), which can be applied to the same applications as before, as well as generating images from rough annotations—a.k.a. doodles.