Generative Adversarial Network (GAN)
Architecture composed of two networks (Generator and Discriminator) that compete to create high-fidelity synthetic data, especially images.
Key Concepts
Generator
A neural network that learns to generate new data that is similar to the training data.
Discriminator
A neural network that learns to distinguish between real data and fake data generated by the generator.
Adversarial Training
The process of training the generator and discriminator together in a zero-sum game, where the generator tries to fool the discriminator and the discriminator tries to identify the fake data.
Nash Equilibrium
A state where neither the generator nor the discriminator can improve their performance by changing their strategy, given the other player's strategy.
Detailed Explanation
A Generative Adversarial Network (GAN) is a type of machine learning model that is used to generate new data that is similar to a given dataset. GANs are composed of two neural networks, a generator and a discriminator, that are trained together in a zero-sum game. The generator tries to create new data that is indistinguishable from the real data, while the discriminator tries to identify the fake data.
The Generator
The generator is a neural network that takes a random noise vector as input and outputs a new data point. The generator is trained to produce data that is as realistic as possible, in order to fool the discriminator.
The Discriminator
The discriminator is a neural network that takes a data point as input and outputs a probability that the data point is real. The discriminator is trained to be as accurate as possible in distinguishing between real and fake data.
Adversarial Training
The generator and discriminator are trained together in a process called adversarial training. In this process, the generator and discriminator are pitted against each other in a zero-sum game. The generator tries to fool the discriminator, while the discriminator tries to identify the fake data. This process continues until the generator is able to produce data that is so realistic that the discriminator is no longer able to distinguish it from real data.
Real-World Examples & Use Cases
Image Generation
GANs can be used to generate realistic images of people, places, and things. This can be used for a variety of applications, such as creating art, generating training data for other machine learning models, and creating virtual worlds.
Video Generation
GANs can be used to generate realistic videos of people, places, and things. This can be used for a variety of applications, such as creating movies, generating training data for other machine learning models, and creating virtual worlds.
Music Generation
GANs can be used to generate realistic music in a variety of genres. This can be used for a variety of applications, such as creating soundtracks for movies and video games, and generating new music for artists to perform.
Text Generation
GANs can be used to generate realistic text, such as news articles, poems, and code. This can be used for a variety of applications, such as creating content for websites and social media, and generating new ideas for writers.