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A direct predecessor of the StyleGAN series is the Progressive GAN, published in 2017. [9]In December 2018, Nvidia researchers distributed a preprint with accompanying software introducing StyleGAN, a GAN for producing an unlimited number of (often convincing) portraits of fake human faces.
FaceGen 3.3 allows the user to randomize, tween, normalize and exaggerate faces, and also includes algorithms for adjusting apparent age, ethnicity and gender. It also allows limited parametric control of facial expressions, and includes a set of phoneme expressions for the animation of characters with "speaking" roles.
Outputs of the generator network from random input were made publicly available on a number of websites. [33] [34] Similarly, since 2018, deepfake technology has allowed GANs to swap faces between actors; combined with the ability to fake voices, GANs can thus generate fake videos that seem convincing. [35]
The similarly AI-driven text adventure game AI Dungeon uses Artbreeder to generate profile pictures for its users, [7] and The Static Age's Andrew Paley has used Artbreeder to create the visuals for his music videos.
There is free software on the market capable of recognizing text generated by generative artificial intelligence (such as GPTZero), as well as images, audio or video coming from it. [83] Potential mitigation strategies for detecting generative AI content include digital watermarking , content authentication , information retrieval , and machine ...
[37] [38] By conditioning the GAN on both random noise and a specific class label, this approach enhanced the quality of image synthesis for class-conditional models. [ 39 ] Autoregressive models were used for image generation, such as PixelRNN (2016), which autoregressively generates one pixel after another with a recurrent neural network . [ 40 ]
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The generator aims to minimize the objective, and the discriminator aims to maximize the objective. The generator's task is to approach , that is, to match its own output distribution as closely as possible to the reference distribution. The discriminator's task is to output a value close to 1 when the input appears to be from the reference ...