Generative Adversarial Networks (GANs)

A Generative Adversarial Network is a type of machine learning system where two neural networks compete against each other, with one generating content and the other trying to distinguish between real and generated samples.

GANs operate through a unique architecture where a generator network creates synthetic data whilst a discriminator network attempts to identify whether the data is genuine or artificial. This adversarial process continues iteratively, with both networks improving their capabilities through competition, much like a counterfeiter and detective trying to outsmart each other.

The generator network learns to create increasingly convincing synthetic data by trying to fool the discriminator, whilst the discriminator becomes better at spotting fakes. This continuous feedback loop results in the generation of highly realistic synthetic content that can be nearly indistinguishable from authentic data.

Examples

• Creating artificial voices
• Creating photorealistic images from descriptions
• Generating synthetic training data
• Converting sketches to photos
• Ageing faces in photographs