All glossary terms
modelsadvanced
Generative Adversarial Network (GAN)
A framework with two neural networks (generator and discriminator) that compete to generate realistic data.
In more detail
Generator creates fake data; discriminator distinguishes fake from real. Adversarial training leads to realistic outputs. Applications: image generation, style transfer, data augmentation. Can be unstable to train.
Examples
- Face generation (StyleGAN)
- Image-to-image translation (CycleGAN)
- Super-resolution
- Data augmentation
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