Glossary

Generative adversarial network (GAN)

What is Generative adversarial network (GAN)?

A generative adversarial network (GAN) consists of two neural networks trained against each other: the generator creates artificial data such as images, and the discriminator tries to tell them apart from real ones. Through this contest the results become increasingly realistic. GANs were introduced in 2014 and shaped image generation before diffusion models became more widespread; they are used, for example, for synthetic training data or image enhancement.

Topics

Neural networks
Image generation
Deep learning

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Related terms

Fréchet inception distance (FID)

The Fréchet inception distance (FID) is a metric for assessing the quality of AI-generated images. Real and generated images are passed through a pre-trained image recognition network (Inception v3) and the statistical distributions of their features are compared. The lower the value, the closer the generated images are to real photos in realism and variety. FID was introduced in 2017 and is a standard for comparing image generators.

Image generation
Evaluation metric
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Deepfake

A deepfake is an image, video or audio clip created or altered with AI that convincingly shows real people saying or doing things that never happened. Alongside legitimate uses in film or dubbing, deepfakes are used for disinformation, reputational damage and fraud – for example fake calls from a supposed manager requesting a bank transfer. Protection includes call-backs via known channels, clear approval processes and labelling of AI-generated content.

disinformation
fraud
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Synthetic data

Synthetic data is generated artificially rather than collected in the real world, but mimics the patterns and properties of real data. It helps when genuine data is scarce, expensive or sensitive from a data protection perspective – for example for testing software, training models or checking a chatbot with realistic sample queries. It is important that it reflects reality closely enough and does not allow conclusions about real people.

test data
data protection
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Template

Generative AI

Generative AI refers to AI systems that create new content – text, images, audio, video or program code – rather than merely sorting or assessing existing data. They are based on models that learn patterns from large amounts of data and use them to produce plausible new outputs. In business it is used, for example, to draft emails, summaries, product descriptions or image ideas. Results can contain errors and should be checked before use.

Core concept
Content creation
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