What is GAN? What GAN architectures do you know and which have you used in practice?

GAN (Generative Adversarial Network) is a type of neural network architecture consisting of two models: a generator and a discriminator, which are trained together in an adversarial manner. The generator tries to create realistic data (e.g., images), while the discriminator aims to distinguish real data from generated data. As a result, the generator learns to produce increasingly plausible data.

Main GAN architectures:

• Vanilla GAN — basic architecture with simple fully connected layers.
• DCGAN (Deep Convolutional GAN) — uses convolutional neural networks, well-suited for images.
• Conditional GAN (cGAN) — generates data conditioned on certain information (e.g., object class).
• Wasserstein GAN (WGAN) — improved training stability by using the Wasserstein distance.
• CycleGAN — for transforming images from one style to another without paired data.

In practice, DCGAN is often used for image generation, and WGAN for more stable training. For example, DCGAN is suitable for generating faces or objects, while WGAN helps avoid issues with vanishing gradients.