Generative Adversarial Networks (GANs) are a class of neural networks that are used for generative models. The structure of a GAN consists of two main components: the Generator and the Discriminator.

Generator

The Generator is responsible for creating new data instances that try to fool the Discriminator. It takes random noise as input and generates synthetic data that can be real or similar to real data.

  • Input: Random noise vector ( z )
  • Output: Synthetic data ( G(z) )

Discriminator

The Discriminator tries to distinguish between real data and synthetic data generated by the Generator. It takes either real data or synthetic data as input and outputs a probability that the input is real.

  • Input: Real data ( x ) or synthetic data ( G(z) )
  • Output: Probability ( D(x) ) that the input is real

Architecture

Here's a basic architecture of a GAN:

  • Generator: ( G(z) )
  • Discriminator: ( D(x) )
  • Loss Functions:
    • For Discriminator: ( L_D(D(x), y) = -\log(D(x)) - \log(1 - D(G(z))) )
    • For Generator: ( L_G(G(z), y) = -\log(D(G(z))) )

Key Components

  • Recurrent Neural Networks (RNNs): Can be used in the Generator to capture temporal dependencies in data.
  • Convolutional Neural Networks (CNNs): Commonly used in both Generator and Discriminator for image generation and classification tasks.
  • Deep Learning Frameworks: Frameworks like TensorFlow and PyTorch are widely used for implementing GANs.

Resources

For more information on GANs and their applications, you can check out our Deep Learning Tutorial.

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GAN Architecture
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