From Doodles To Paintings With Deep Learning | Two Minute Papers #57
This technique uses deep learning to create beautiful paintings from terribly drawn sketches. The results look so great that many people called this work out to be an April Fools' day joke!

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The paper 'Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artwork' and its implementation is available here:
https://github.com/alexjc/neural-doodle
http://arxiv.org/pdf/1603.01768v1.pdf

A playlist with out neural network and deep learning-related videos:
https://www.youtube.com/playlist?list=PLujxSBD-JXglGL3ERdDOhthD3jTlfudC2

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 Dear Fellow Scholars, this is Two Minute Papers with Karoji Zorna Ifehir. This is the first paper in Two Minute Papers that showcased so stunning results that people called it out to be an April Fool's Day joke. It is based on a deep neural network and the concept is very simple. You choose an artistic style, you make a terrible drawing, and it creates a beautiful painting out of it. If you would like to know more about deep neural networks, we've had a ton of fun with them in previous episodes, I've put a link to them in the description box. I expect an onslaught of magnificent results with this technique to appear very soon. It is important to note that one needs to create a semantic map for each artistic style, so that the algorithm learns the correspondence between the painting and the semantics. However, these maps have to be created only once and can be used forever, so I expect quite a few of them to show up in the near future, which greatly simplifies the workflow. After that, these annotations can be changed at will, you press a button, and the rest is history. Woah! Wicked results. Some of these neural art results look so good that we should be creating a new class of touring tests for paintings. This means that we are presented with two images, one of them is painted by a human and one by a computer. We need to click the ones that we think were painted by a human. Damn, curses. As always, these techniques are new and heavily experimental, and this usually means that they take quite a bit of time to compute. The presentation video you have seen was sped up considerably. If these works are worthy of further attention, and I definitely think they are, then we can expect great strides towards interactivity in follow-up papers very soon. I am really looking forward to it, and we fellow scholars will have a ton of fun with these tools in the future. Thanks for watching and for your generous support, and I'll see you next time.
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