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Foundations

Introduction to Generative Models
Neural Network Foundations for Image Generation
Random Variables, Latent Variables, and Deep Latent Variable Models
Evaluating Generated Images: FID, KID, and Practical Cautions

Variational Autoencoders

Variational Autoencoders: ELBO, Inference, and Learning
Variational Autoencoder Variants and Extensions

Generative Adversarial Networks

Generative Adversarial Networks
GAN Variants and Modern Practice

Diffusion Models

Discrete Diffusion Models
Score-Based Models, SDEs, and Probability Flow ODEs
Prompted Generation, Classifier-Free Guidance, and Latent Diffusion

Flow Matching

Flow Matching
Flow Matching Variants and Modern Practice

Setup and References

Environment Setup
Further Reading

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By Davide Evangelista

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