Generative Adversarial Networks (GANs) are algorithmic architectures that use two neural networks, pitting one against the other to generate new, synthetic instances of data that can pass for accurate data. The Generative Adversarial Network trains a generative model by framing the problem as a supervised learning problem with two sub-models: the generator model that we train to generate new examples and the discriminator model that tries to classify examples as either real or fake. GANs can create images that look like human faces, even though the faces don’t belong to any natural person, living or dead. Use EdrawMax to create Generative Adversarial Network diagrams.
