# NVIDIA's AI Dreams Up Imaginary Celebrities! 👨‍⚖️ ## Метаданные - **Канал:** Two Minute Papers - **YouTube:** https://www.youtube.com/watch?v=VrgYtFhVGmg - **Дата:** 18.11.2017 - **Длительность:** 3:49 - **Просмотры:** 72,785 - **Источник:** https://ekstraktznaniy.ru/video/14554 ## Описание The paper "Progressive Growing of GANs for Improved Quality, Stability, and Variation" and its source code is available here: http://research.nvidia.com/publication/2017-10_Progressive-Growing-of Our Patreon page with some really cool perks: https://www.patreon.com/TwoMinutePapers We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Andrew Melnychuk, Brian Gilman, Christoph Jadanowski, Dave Rushton-Smith, Dennis Abts, Eric Haddad, Esa Turkulainen, Evan Breznyik, Kaben Gabriel Nanlohy, Malek Cellier, Michael Albrecht, Michael Jensen, Michael Orenstein, Raul Araújo da Silva, Robin Graham, Steef, Steve Messina, Sunil Kim, Torsten Reil. https://www.patreon.com/TwoMinutePapers Two Minute Papers Merch: US: http://twominutepapers.com/ EU/Worldwide: https://shop.spreadshirt.net/TwoMinutePapers/ Music: Antarctica by Audionautix is licensed under a Creative Commons Attribution license (https://creativecommons.org/licenses/by/4.0/) Artist: http://audiona ## Транскрипт ### [] Dear Fellow Scholars, this is Two Minute Papers with Károly Zsolnai-Fehér. Hold on to your papers because these results are completely out of this world, you'll soon see why. In this work, high-resolution images of imaginary celebrities are generated via a generative adversarial network. This is an architecture where two neural networks battle each other: the generator network is the artist who tries to create convincing, real-looking images and the discriminator network, the critic tries to tell a fake image from a real one. The artist learns from the feedback of the critic and will improve itself to come up with better quality images, and in the meantime, the critic also develops a sharp eye for fake images. These two adversaries push each other until they are both adept at their tasks. A classical drawback of this architecture is that it is typically extremely slow to train and these networks are often quite shallow, which means that we get low-resolution images that are devoid of sharp details. However, as you can see here, these are high resolution images with tons of details. So, how is that possible? So here comes the solution from scientists at NVIDIA. Initially, they start out with tiny, shallow neural networks for both the artist and the ### Progressive growing [1:14] critic, and as time goes by, both of these neural networks are progressively grown. They get deeper and deeper over time. This way, the training process is more stable than using deeper neural networks from scratch. It not only generates pictures, but it can also compute high resolution intermediate images via latent space interpolation. It can also learn object categories from a bunch of training data and generate new samples. And, if you take a look at the roster of scientists on this project, you will see that they are ### Latent space interpolations [1:45] computer graphics researchers who recently set foot in the world of machine learning. And man, do they know their stuff and how to present a piece of work! And now comes something, that is the absolute most important part of the evaluation that should be a must for every single paper in this area. ### Generated images [2:06] These neural networks were trained on a bunch of images of celebrities, and are now generating new ones. However, if all we are shown is a new image, we don't know how close it is to the closest image in the training set. If the network is severely overfitting, it would essentially copy/paste samples from there. Like a student in class who hasn't learned a single thing, just memorized the textbook. Actually, what is even worse is that this would mean that the worst learning algorithm that hasn't learned anything but memorized the whole database would look the best! That's not useful knowledge. And here you see the nearest neighbors, the images that are the closest in this database to the newly synthesized images. It shows really well that the AI has learned the concept of a human face extremely well and can synthesize convincing looking new images that are not just copy-pasted from the training set. The source code, pre-trained network and one hour of imaginary celebrities are also available in the description, check them out! Premium quality service right there. And, if you feel that 8 of these videos a month is worth a dollar, please consider supporting us on Patreon. You can also get really cool additional perks like early access, and it helps us to make better videos, grow, and tell these incredible stories to a larger audience. Details are available in the description. Thanks for watching and for your generous support, and I'll see you next time!