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AI Core Concepts (Part 7): Generative Models

Generative Models learn the underlying patterns of input data to generate new data that resembles the training distribution. They are used in image synthesis, text generation, audio creation, drug discovery, and more.

1. What are Generative Models?

Unlike discriminative models (which learn P(y|x)), generative models try to model the data distribution itself (P(x) or P(x|z)), allowing them to sample new instances.

2. Common Types of Generative Models

🔹 Variational Autoencoders (VAEs)

Learn to encode input data into a latent space, then decode it back.
Use probabilistic encoders and KL-divergence in loss.

Example: VAE loss

loss = reconstruction_loss + KL_divergence

VAE Applications: Denoising, image generation, latent space exploration.

🔹 Generative Adversarial Networks (GANs)

Consist of a generator and discriminator in a zero-sum game.
Generator tries to fool the discriminator into thinking fake samples are real.

Training loop overview

# Train discriminator
loss_D = loss(real) + loss(fake)

# Train generator
loss_G = loss(fooling_discriminator)

Popular GAN variants: DCGAN, StyleGAN2, CycleGAN.

🔹 Autoregressive Models

Generate output one token at a time, conditioned on previous tokens.
Examples: PixelRNN, WaveNet, GPT

Example: Autoregressive text generation with GPT-2

from transformers import pipeline

generator = pipeline("text-generation", model="gpt2")
output = generator("Once upon a time", max_length=30)
print(output[0]["generated_text"])

🔹 Diffusion Models

Learn to reverse a noise process to generate high-quality data.
Dominating image generation (e.g., DALL·E 3, Stable Diffusion).

Core idea: Train model to gradually remove noise from random input.

Frameworks:

diffusers by Hugging Face
stable-diffusion-webui

3. Applications of Generative Models

🎨 Art and Design (DALL·E, Midjourney)
🧬 Drug Discovery (generating novel molecules)
🧠 Data Augmentation (synthesizing training examples)
🗣️ Text-to-Speech (e.g., Tacotron, WaveNet)
📈 Anomaly Detection (model normal data and flag unusual ones)

4. Common Challenges

Mode collapse (GANs generate limited diversity)
Training instability (especially with adversarial setups)
Evaluating quality of generated data
- Inception Score (IS)
- Frechet Inception Distance (FID)
- Human judgment for language or creative tasks

📚 Further Resources

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