# These Neural Networks Empower Digital Artists ## Метаданные - **Канал:** Two Minute Papers - **YouTube:** https://www.youtube.com/watch?v=DuMmcVOsNcs - **Дата:** 29.09.2018 - **Длительность:** 3:14 - **Просмотры:** 25,099 ## Описание The paper "Differentiable Image Parameterizations" is available here: https://distill.pub/2018/differentiable-parameterizations/ Distill editorial article - see how you can contribute here: https://distill.pub/2018/editorial-update/ Pick up cool perks on our Patreon page: https://www.patreon.com/TwoMinutePapers We would like to thank our generous Patreon supporters who make Two Minute Papers possible: 313V, Andrew Melnychuk, Angelos Evripiotis, Anthony Vdovitchenko, Brian Gilman, Christian Ahlin, Christoph Jadanowski, Dennis Abts, Emmanuel, Eric Haddad, Eric Martel, Esa Turkulainen, Evan Breznyik, Geronimo Moralez, John De Witt, Kjartan Olason, Lorin Atzberger, Marten Rauschenberg, Michael Albrecht, Michael Jensen, Milan Lajtoš, Morten Punnerud Engelstad, Nader Shakerin, Owen Skarpness, Raul Araújo da Silva, Rob Rowe, Robin Graham, Ryan Monsurate, Shawn Azman, Steef, Steve Messina, Sunil Kim, Thomas Krcmar, Torsten Reil, Zach Boldyga. https://www.patreon.com/TwoMinutePapers Two Minute Papers Merch: US: http://twominutepapers.com/ EU/Worldwide: https://shop.spreadshirt.net/TwoMinutePapers/ Splash screen/thumbnail design: Felícia Fehér - http://felicia.hu Károly Zsolnai-Fehér's links: Facebook: https://www.facebook.com/TwoMinutePapers/ Twitter: https://twitter.com/karoly_zsolnai Web: https://cg.tuwien.ac.at/~zsolnai/ ## Содержание ### [0:00](https://www.youtube.com/watch?v=DuMmcVOsNcs) Segment 1 (00:00 - 03:00) Dear Fellow Scholars, this is Two Minute Papers with Károly Zsolnai-Fehér. In this series, we have seen many times how good neural network-based solutions are at image classification. This means that the network looks at an image and successfully identifies its contents. However, neural network-based solutions are also capable of empowering art projects by generating new, interesting images. This beautifully written paper explores how a slight tweak to a problem definition can drastically change the output of such a neural network. It shows how many of these research works can be seen as the manifestation of the same overarching idea. For instance, we can try to visualize what groups of neurons within these networks are looking for, and we get something like this. The reason for this is that important visual features, like the eyes can appear at any part of the image and different groups of neurons look for it elsewhere. With a small modification, we can put these individual visualizations within a shared space and create a much more consistent and readable output. In a different experiment, it is shown how a similar idea can be used with Compositional Pattern Producing Networks, or CPPNs in short. These networks are able to take spatial positions as an input and produce colors on the output, thereby creating interesting images of arbitrary resolution. Depending on the structure of this network, it can create beautiful images that are reminiscent of light-paintings. And here you can see how the output of these networks change during the training process. They can also be used for image morphing as well. A similar idea can be used to create images that are beyond the classical 2D RGB images, and create semi-transparent images instead. And there is much, much more in the paper, for instance, there is an interactive demo that shows how we can seamlessly put this texture on a 3D object. It is also possible to perform neural style transfer on a 3D model. This means that we have an image for style, and a target 3D model, and, you can see the results over here. This paper is a gold mine of knowledge, and contains a lot of insights on how neural networks can further empower artists working in the industry. If you read only one paper today, it should definitely be this one, and this is not just about reading, you can also play with these visualizations, and as the source code is also available for all of these, you can also build something amazing on top of them. Let the experiments begin! So, this was a paper from the amazing Distill journal, and just so you know, they may be branching out to different areas of expertise, which is amazing news. However, they are looking for a few helping hands to accomplish that, so make sure to click the link to this editorial update in the video description to see how you can contribute. I would personally love to see more of these interactive articles. Thanks for watching and for your generous support, and I'll see you next time! --- *Источник: https://ekstraktznaniy.ru/video/14410*