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A generative adversarial network (GAN) is a class of machine learning frameworks designed by Ian Goodfellow and his colleagues in 2014. 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). ... and then down-sampling them to fit the game's native resolution (with results ...
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The hope is that as the two networks face off, they'll both get better and ... GAN Lab visualizes gradients (as pink lines) for the fake samples such that the ...
Researchers continue to find improved GAN techniques and new uses for GANs. ... The system requires no labels or pairwise correspondences between images. ... its own plausible pattern to replace the pattern erased by the down-sampling.
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24 May 2019 ... data), the generator is tasked with generating “fake” data resembling ... layers of roughly the same size to sample an image up or down.
10 Feb 2020 ... A GAN can have two loss functions: one for generator training and one for ... "Wasserstein GAN" or "WGAN") in which the discriminator does not ...
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