1. Generative Adversarial Networks (GAN)

Details:

• Concept: GANs involve two neural networks, a generator and a discriminator, that are trained simultaneously. The generator tries to create data that mimics real data, while the discriminator tries to distinguish between real and fake data.

Architecture:

• Generator (G):
  • Input: Noise vector z sampled from a predefined distribution (e.g., Gaussian).
  • Layers: Fully connected or convolutional layers.
  • Output: Synthetic data point.
• Discriminator (D):
  • Input: Real or synthetic data.
  • Layers: Fully connected or convolutional layers.
  • Output: Scalar value indicating the probability that the input is real.

Modifications:

• Various architectural enhancements like Deep Convolutional GANs (DCGANs), Wasserstein GANs (WGANs), etc., to improve training stability and performance.

Use Cases:

• Image synthesis, text-to-image generation, image inpainting, and various scientific applications.

Conditional GAN (cGAN)

Details:

• Concept: cGANs are an extension of GANs where both the generator and discriminator are conditioned on some additional information.

Architecture:

• Generator (G):
  • Input: Noise vector z concatenated with a class label c.
  • Layers: Similar to GAN but conditioned on c.
  • Output: Synthetic data conditioned on c.
• Discriminator (D):
  • Input: Real or synthetic data concatenated with class label c.
  • Layers: Similar to GAN but conditioned on c.
  • Output: Scalar value indicating the probability that the input is real.

Modifications:

• Conditional information can be any additional data like class labels, text descriptions, etc.