2.3 Generative AI Architectures

Generative AI models create new, synthetic data based on existing data. The three key architectures in Generative AI are:

• Generative Adversarial Networks (GANs)
• Variational Autoencoders (VAEs)
• Transformers

1. Generative Adversarial Networks (GANs)

• Main Idea: Two networks (Generator and Discriminator) work together in competition.
  • Generator: Creates fake data from random noise.
  • Discriminator: Identifies if the data is real or fake.
  • Adversarial Process: The Generator tries to fool the Discriminator while the Discriminator improves in detecting fake data.
• Goal: Generator produces data that’s indistinguishable from real data.

Applications:

• Image generation (e.g., DeepFakes)
• Style transfer
• Text-to-image generation

2. Variational Autoencoders (VAEs)

• Main Idea: Learns to represent data in a lower-dimensional latent space and generate new data from it.
  • Autoencoder: Compresses data and reconstructs it back to its original form.
  • Variational Aspect: Introduces probabilistic elements to learn smooth, continuous latent space.
  • Latent Space: Allows generation of new data by sampling from the learned distribution.
  • Reconstruction Loss: Minimize difference between original and reconstructed data, while ensuring the latent space follows a probability distribution (Gaussian).

Applications:

• Image generation
• Anomaly detection
• Drug discovery

3. Transformers

• Main Idea: Designed for handling sequential data (text, audio, etc.), using the self-attention mechanism to capture relationships between words or tokens.
  • Self-Attention: Weighs importance of each word in relation to others.
  • Encoder-Decoder Architecture:
    • Encoder: Processes the input.
    • Decoder: Generates the output.
  • Position Encoding: Adds position information since transformers don’t process data sequentially.

Key Models:

• GPT (Generative Pretrained Transformer): Predicts the next word in a sentence, used for text generation.
• BERT (Bidirectional Encoder Representations): Understands context in both directions, used for tasks like question answering.

Applications:

• Natural Language Processing (NLP): Text generation, summarization, translation, etc.
• Text-to-image generation (e.g., DALL·E)
• Speech processing

Comparison Table

Conclusion

• GANs are ideal for generating high-quality images.
• VAEs are best for structured data generation with smooth latent spaces.
• Transformers excel in handling and generating sequential data, especially for NLP.

Understanding these architectures helps in selecting the right model for your generative tasks!