Datagrid says, "We have succeeded in generating high-resolution (1024×1024) images of whole-body who don't exist using Generative Adversarial Networks (GANs). We use these images as virtual models for advertising and fashion." Read the rest
Generative Adversarial Networks use a pair of machine-learning models to create things that seem very realistic: one of the models, the "generator," uses its training data to make new things; and the other, the "discerner," checks the generator's output to see if it conforms to the model.
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Generative Adversarial Networks use a pair of machine-learning models to create things that seem very realistic: one of the models, the "generator," uses its training data to make new things; and the other, the "discerner," checks the generator's output to see if it conforms to the model. Read the rest
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Like many internet addicts, I was blown away by NVIDEO’s demo using style-based Generative Adversarial Networks to generate faces. They seem to have crossed a threshold for generating artificial images that can genuinely fool our brains.
Flash forward to last week when I saw Philip Wang’s amazing single-serving website thispersondoesosxist.com.
It was fascinating to see a photo-realistic face of someone that doesn’t exist. Philip’s writeup does a great job explaining his motivations and this implications behind this groundbreaking technology.
But I just wanted to turn it into hot or not.
So I wrote a script to download an image from thispersondoesnotexist.com every 5 seconds and built up a collection of around two thousand fake people. Then I made a voting system with php/MySQL and some filters to show the highest and lowest rated faces. And I enabled comments just for fun.
First, take this quiz to see how good you are at distinguishing between real faces and fake ones made with generative adversarial networks (GANs). Then, read this article that teaches you how to spot the fakes. In a few years AI will be able to generate images that don't have recognizable tells. Read the rest
Jason Antic's DeOldify is a Self-Attention Generative Adversarial Network-based machine learning system that colorizes and restores old images. It's only in the early stages but it's already producing really impressive results, and the pipeline includes a "defade" model that is "just training the same model to reconstruct images that augmented with ridiculous contrast/brightness adjustments, as a simulation of fading photos and photos taken with old/bad equipment." Read the rest
This blurry portrait of a man may not look like much but it just sold at auction for $432,500, nearly 45 times its high estimate. What makes it so special? The Portrait of Edmond Belamy is the work of Artificial Intelligence and it's the first of its kind to sell at a major auction house.
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This portrait, however, is not the product of a human mind. It was created by an artificial intelligence, an algorithm defined by that algebraic formula with its many parentheses. And when it went under the hammer in the Prints & Multiples sale at Christie’s on 23-25 October, Portrait of Edmond Belamy sold for an incredible $432,500, signalling the arrival of AI art on the world auction stage.
The painting, if that is the right term, is one of a group of portraits of the fictional Belamy family created by Obvious, a Paris-based collective consisting of Hugo Caselles-Dupré, Pierre Fautrel and Gauthier Vernier. They are engaged in exploring the interface between art and artificial intelligence, and their method goes by the acronym GAN, which stands for ‘generative adversarial network’.
‘The algorithm is composed of two parts,’ says Caselles-Dupré. ‘On one side is the Generator, on the other the Discriminator. We fed the system with a data set of 15,000 portraits painted between the 14th century to the 20th. The Generator makes a new image based on the set, then the Discriminator tries to spot the difference between a human-made image and one created by the Generator.