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.
What does "Generative Adversarial Network (GAN)" mean?
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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