Explanation
Imagine two artists working together, but with opposing goals.
One artist, the "Generator", tries to create original paintings that look like they came from a famous master.
The other artist, the "Discriminator", acts as an art critic, trying to distinguish between the real masterpieces and the Generator's forgeries.
As they continue this process, the Generator gets better at creating convincing fakes, and the Discriminator becomes more skilled at spotting them.
This back-and-forth competition drives both artists to improve.
Generative Adversarial Networks (GANs) work on the same principle. They're a type of machine learning architecture where two neural networks, a generator and a discriminator, compete against each other. This allows the system to learn to generate new, realistic data that resembles the training data.
Examples
Consumer Example
Think of those apps that let you 'age' your face or turn a photo into a cartoon.
GANs are often behind these fun transformations.
The generator creates the altered image, while the discriminator ensures it looks realistic and convincing.
It's how you can see what you might look like in 50 years, or as a character in your favourite animated film.
Business Example
Consider a fashion company designing new clothing lines.
GANs can be trained on thousands of existing designs to generate entirely new and original clothing patterns.
Designers can then refine these AI-generated designs, speeding up the creative process and exploring ideas they might never have considered otherwise.
It’s like having an AI design assistant, constantly suggesting fresh and innovative concepts.