Specifically, the problem is framed in terms of a generator network and a discriminator network. The generator tries to generate things that fool the discriminator into classifying the generated images as being from a dataset only seen by the discriminator. The results have been pretty amazing, with GANs used to generate everything from images, to audio samples, to code snippets. But GAN training can also be quite unstable, and prior methods have struggled to generate high-resolution images. All existing layers in both networks remain trainable throughout the training process.
But not exactly. She appears to be a celebrity, one of the beautiful people photographed outside a movie premiere or an awards show. And yet you cannot quite place her. The image is one of the faux celebrity photos generated by software under development at Nvidia, the Santa Clara computer chipmaker that is investing heavily in research involving artificial intelligence.
A study on the relevance of density-based anomaly detection methods. A personal journey in generative modelling research. Stochastic augmentation of normalizing flows.
A generative adversarial network GAN is a class of machine learning frameworks designed by Ian Goodfellow and his colleagues in 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.