🧠 The Backbone of Generative AI

Generative AI relies on several key architectures, each tailored to different tasks and data types:

🔁 a. Generative Adversarial Networks (GANs)

These consist of two neural networks—the generator and the discriminator—that compete in a minimax game.

• The generator creates fake data instances aiming to fool the discriminator.
• The discriminator evaluates their authenticity.
  Over time, this adversarial training yields highly realistic data, especially in image synthesis.

🌀 b. Variational Autoencoders (VAEs)

VAEs encode data into a low-dimensional latent space from which new data can be sampled.
They optimize a variational lower bound to maximize the likelihood of data, enabling smooth interpolations and controlled generation.

⚙️ c. Transformer-based Models

Transformers, especially large language models like GPT, use self-attention mechanisms to capture long-range dependencies in sequential data.
They are pre-trained on massive datasets and fine-tuned for specific tasks, excelling in text generation and translation.

🧩 Diagram: Basic architecture of GAN

   Noise Vector
       |
       v
[Generator] ---> Fake Data  ---> [Discriminator]
                           ^                |
                           |                v
                Real Data   <----------- Discriminator