Importance

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Abstract

Systems, apparatuses, and methods directed to training a Generator that is part of a Generative Adversarial Network (GAN) to generate “realistic” examples of a distribution. In some embodiments, this includes training the Generator model to receive a tensor as an input and generate a distribution which is then converted to the frequency domain and used to determine a loss term. Similarly, an actual distribution is also converted to the frequency domain and used to determine a loss term. The loss terms, generated distribution, and actual distribution are provided to a Discriminator which generates loss terms used as part of a backpropagation or feedback mechanism to modify the operation of the Generator and/or Discriminator.

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First Claim