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What is Understanding GAN Components and Functions about?

Generative Adversarial Networks (GANs) consist of two main components: the Generator, which creates synthetic data from random noise, and the Discriminator, which distinguishes between real and fake data. The training process involves an iterative adversarial loop where both networks improve through competition, leading to the generation of highly realistic outputs. Applications of GANs span various fields, including image generation, medical imaging, and data augmentation.

Author

Anisha

Language

EN