Artificial intelligence points where we are able to create very realistic images of people, of course, these people do not exist. NVIDIA, for example, is one of the companies that used artificial intelligence to generate images of non-existent humans on the planet.
All the faces on this site are completely fake, created using a special type of artificial intelligence algorithms called GANs. You can of course look at this technique with the following website: thispersondoesnotexist.com
Whenever you refresh the page in this site an algorithm will generate a completely false real image of someone. The page was prepared by Uber Philip Wang software engineer to demonstrate what GANs can do.
How it works
All GAN networks have two networks: alternator and semiconductor. The generator collects new samples from scratch and takes samples from both the training data and generator outputs and predicts whether they are "real" or "fake".
How it works
All GAN networks have two networks: alternator and semiconductor. The generator collects new samples from scratch and takes samples from both the training data and generator outputs and predicts whether they are "real" or "fake".
This artificial intelligence learns to collect more "realism" images. At the same time, the discriminant also learns by comparing samples generated with real samples, making it difficult for the generator to deceive.
The GAN was introduced in 2014, but until 2017 researchers were unable to create high-quality images of 1024x1024 detailed in the current ProGAN paper. StyleGAN relies on this previous work, but now allows researchers more control over specific features.
The GAN was introduced in 2014, but until 2017 researchers were unable to create high-quality images of 1024x1024 detailed in the current ProGAN paper. StyleGAN relies on this previous work, but now allows researchers more control over specific features.
This artificial intelligence learns to collect more "realism" images. At the same time, the discriminant also learns by comparing samples generated with real samples, making it difficult for the generator to deceive.
The GAN was introduced in 2014, but until 2017 researchers were unable to create high-quality images of 1024x1024 detailed in the current ProGAN paper. StyleGAN relies on this previous work, but now allows researchers more control over specific features.
The GAN was introduced in 2014, but until 2017 researchers were unable to create high-quality images of 1024x1024 detailed in the current ProGAN paper. StyleGAN relies on this previous work, but now allows researchers more control over specific features.
