GAN

Generative Adversarial Networks (GANs) are a type of neural network that are particularly useful for generating new data, such as images, text, or audio. They are designed to generate data that is similar to a training dataset, by learning the patterns and features of the data.

History:

GANs were first introduced in 2014 by Ian Goodfellow and Yoshua Bengio.

Architecture:

%%{init: {"flowchart": {"htmlLabels": false}} }%% graph LR A[Input] -->|"Generator Network"| B[Generated Image] A -->|"Discriminator Network"| C[Real/Fake Decision] C -->|"Feedback"| A B -->|"Evaluation"| C