/qpatriotresearch/ - Q Patriot Research

Generative adversarial network

A generative adversarial network (GAN) is a class of machine learning systems invented by Ian Goodfellow and his colleagues in 2014.[1] Two neural networks contest with each other in a game (in the sense of game theory, often but not always in the form of a zero-sum game). Given a training set, this technique learns to generate new data with the same statistics as the training set. For example, a GAN trained on photographs can generate new photographs that look at least superficially authentic to human observers, having many realistic characteristics. Though originally proposed as a form of generative model for unsupervised learning, GANs have also proven useful for semi-supervised learning,[2] fully supervised learning,[3] and reinforcement learning.[4] In a 2016 seminar, Yann LeCun described GANs as "the coolest idea in machine learning in the last twenty years".[5]

https://arxiv.org/abs/1912.04958

"This person doesn't exist": https://www.thispersondoesnotexist.com

Note: Every time you load/reload the page it generates a new fake person. Good for creating fake identities, in google, facecrook etc..

Now a version of molecules: https://thischemicaldoesnotexist.com

Now a version of cats: https://thiscatdoesnotexist.com

Now a version of horses: https://thishorsedoesnotexist.com

Now a version of articles: https://thisarticledoesnotexist.com

Now a version of John James Pratt (LOL): https://thispersondoesnotexist.com/friends/

Now a version of Office: https://thispersondoesnotexist.com/office/

Wrap that around your head!!! You cannot imagine the potential of this...