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Letter

Letter G
Generative Adversarial Network (GAN)

A Generative Adversarial Network (GAN) is a type of deep learning model comprising two neural networks—the generator and the discriminator—that compete against each other. The generator creates new data instances, such as images, while the discriminator evaluates them for authenticity against a dataset.

Use Cases

Image Generation

Generating realistic images of faces, objects, or scenes.

Video Generation

Creating synthetic videos based on existing footage.

Data Augmentation

Generating new examples to increase the diversity of training data.

Importance

Creativity

Enables the creation of new, realistic data that resembles the original dataset.

Unsupervised Learning

Provides a framework for learning without labelled data

Potential Applications

Extensively used in creative fields, security (e.g., generating adversarial examples), and data augmentation.

Analogies

A GAN is like a forger and an art critic in an ongoing contest. The forger tries to create realistic paintings, while the critic tries to distinguish them from real artworks. This competition drives both to improve their abilities over time.

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