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What are the key components of Generative Adversarial Networks (GANs)?
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Generative Adversarial Networks (GANs) are a class of machine learning frameworks designed to generate realistic data samples by leveraging two neural networks that work against each other in a competitive process.
Introduced by Ian Goodfellow in 2014, GANs have become highly effective in generating images, videos, and even text. The primary goal of GANs is to learn the distribution of a dataset and generate new data that mimics the characteristics of the original data.
GANs consist of two key components: the Generator and the Discriminator. These two networks are trained simultaneously in a zero-sum game framework, where the Generator tries to create data that looks real, and the Discriminator attempts to distinguish between real and generated (fake) data.
Key Components of GANs:
- Generator:
