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The Wasserstein GAN (WGAN) is an algorithm introduced in a paper written by Martin Arjovsky, Soumith Chintala and Léon Bottou at the Courant Institute of Mathematical Sciences. The paper examines methods for unsupervised learning, and provides part of the roadmap for dealing with the pursuit of certain outcomes in machine learning projects.
The Wasserstein GAN algorithm is a variation of generative adversarial networks (GANs). Generative adversarial networks feature capabilities related to discriminating between data sets and choosing outcomes are fundamentally useful in machine learning. The Wasserstein GAN is a specific kind of GAN that, according to the team, “minimizes a reasonable and efficient approximation of the Earth Mover’s distance,” where the EM distance is a method to look at dissimilarity between two multidimensional data sets.
Through helping to deal with major training problems of generative adversarial networks in general, the Wasserstein GAN can be useful in the pursuit of dimensionality reduction and other goals related to specific machine learning outcomes.