The Wasserstein GAN (WGAN), introduced in 2017 by Martin Arjovsky and collaborators, revolutionised GAN training by addressing instability and mode collapse using the Wasserstein distance. Its applications range from generating realistic images to synthetic data creation, with significant impacts globally and in Australia.
