GLOSSARYGenerative Adversarial Networks (GANs)
Generative Adversarial Networks (GANs) are algorithmic frameworks that use deep learning to generate new digital content.
What are Generative Adversarial Networks (GANs)?
Generative Adversarial Networks (GANs) are algorithmic frameworks that use deep learning to generate new digital content. These are typically applied to generative AIs and the ‘adversarial’ part of the name comes from the fact that two neural networks are ‘competing’ against one another: a generator network and a discriminator network.
During training, the generator and discriminator networks battle against one another with the generator trying to create data that the discriminator cannot distinguish from the real training data, and the discriminator trying to accurately identify generated data. This competition drives the generator network to improve its ability to generate realistic data.
GANs have a wide range of potential applications, including generating realistic images, improving machine translation tasks, and creating personalized content. GANs can also be used to create fake or misleading content, such as deepfakes. It is increasingly important to verify that the images or video before you are unaltered and physically exist.
