AI Termcirca 2014· Added Jun 6, 2026
Generative Adversarial Network (GAN)
A GAN is a machine learning model used to generate new data by pitting two neural networks against each other.
Generative Adversarial Networks (GANs) are a class of machine learning frameworks designed to produce new data instances that mimic a given dataset. They consist of two parts: a generator that creates data and a discriminator that evaluates the authenticity of the generated data. The goal is for the generator to produce data indistinguishable from the real dataset, effectively fooling the discriminator. This adversarial process continues until the generator produces highly realistic outputs.
Examples
- Creating realistic human faces from scratch using GANs.
- Enhancing low-resolution images to high-resolution through super-resolution GANs.
- Generating artwork or novel designs in fashion.
Common misconceptions
- GANs do not merely copy input data—they create new, unique outputs.
- The discriminator does not refine images; it only evaluates them.
- GANs are not limited to image generation; they can be used for other types of data as well.
Related terms
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