The generator is usually a multilayer perceptron (neural network made up of multiple layers) and its goal is to create data as close as possible to real data in order to fool the discriminator when it evaluates them.
The generator is usually a multilayer perceptron (neural network made up of multiple layers) and its objective is to create data as close as possible to real data in order to deceive the discriminator when it evaluates them.
The discriminator is usually a convolutional neural network, in charge of classifying incoming data as real or not, assigning each one a probability of being real. In the training phase of the neural network, the discriminator will receive both data generated by the generator and data belonging to a training set, and tries to distinguish between them.
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