This repository provides a comprehensive tutorial and demonstration of generative models in machine learning, targeting students and researchers interested in unsupervised learning. It covers foundational concepts and popular architectures like VAEs and GANs, offering a structured learning path with explanations, code references, and links to key papers and courses.

How It Works

The tutorial progresses from basic sampling techniques and Bayesian classifiers to more advanced models like Gaussian Mixture Models (GMMs), Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs). It explains the underlying theory, mathematical formulations (e.g., VAE's ELBO, GAN's cost functions), and architectural details of various GAN variants (DCGAN, CycleGAN, Pix2Pix, etc.) and auto-regressive models (PixelRNN, PixelCNN). The approach emphasizes understanding the evolution and distinct characteristics of these generative paradigms.

Quick Start & Requirements

• Installation: Primarily conceptual; no direct installation commands are provided for a runnable demo. The content references libraries like Scipy, Tensorflow, and Scikit-learn.
• Prerequisites: Familiarity with neural network concepts (gradient descent, cost functions, activation functions), regression, and classification is expected.
• Resources: The tutorial itself is text-based, but implementing the described models typically requires significant computational resources (GPUs) and large datasets.
• Links:
  • Blog Open-AI: https://openai.com/blog/
  • Udemy GAN-VAE: https://www.udemy.com/course/deep-learning-gans-and-variational-autoencoders/
  • Stanford Generative Model Video: https://www.youtube.com/watch?v=2_h_h_f7n_Q
  • Tensorflow-Generative-Model-Collections-Codes: https://github.com/carpedm20/deep-learning-models

Highlighted Details

• Detailed breakdown of GAN architectures including DCGAN, CycleGAN, Pix2Pix, and others for image-to-image translation, super-resolution, and style transfer.
• Explanation of auto-regressive models like PixelRNN and PixelCNN, highlighting their advantages in explicit probability calculation and training stability over GANs.
• Coverage of VAEs, including their latent space representation and cost function (ELBO).
• Discussion of generative models in reinforcement learning, specifically Generative Adversarial Imitation Learning.

Maintenance & Community

• The README states the tutorial "will continue to be updated over time."
• No specific community links (Discord, Slack) or active contributor information are provided.

Licensing & Compatibility

• The README does not explicitly state a license.
• The content is marked "only for education purpose. It is not academic study/paper."

Limitations & Caveats

This repository serves as a tutorial and reference, not a directly executable code library. While it lists many papers and concepts, it does not provide runnable code examples or a unified framework for experimentation, requiring users to find and implement the models themselves.