Spring 2022 - Web 3:10-6:00 PM, Peking University
This course covers the fundamentals, research topics and applications of deep generative models.
Schedule
Fundamentals
Week 1 | Introduction | Lecture 1: Introduction Lecture 2: Mathematic Foundation & Basic Concept Lecture 3: Data Representation |
Week 2 | Autoregressive Models | Lecture 4: Sequential Models - Recurrent Neural Networks Lecture 5: Autoregressive Models 1 Lecture 6: Autoregressive Models 2 |
Week 3 | Variational Autoencoders | Lecture 7: From Autoencoder to VAE Lecture 8: Variational Autoencoder Lecture 9: VAE Variants |
Week 4 | Normalising Flow Models | Lecture 10-11: Normalising Flow Background Lecture 12: Normalising Flow Models |
Week 5 | Generative Adversarial Networks | Lecture 13: Vanilla GAN Lecture 14: Understanding GAN Lecture 15: Selected GANs |
Research & Application
Week 6 | Evaluation of Generative Models | Lecture 16-17: Sampling Quality Lecture 17-18: Density Evaluation & Latent Representation |
Week 7 | Energy-based Models | Lecture 19: Hopfield Network Lecture 20: Boltzmann Machine Lecture 21: Energy-based GANs |
Week 8 | Challenges of Generative Models | Lecture 22: High-dimensional Data Generation Lecture 23: Learning Large Encoder Lecture 24: Other Challenges |
Week 9 | Applications of Generative Models | Lecture 25: Image Synthesis, Translation and Manipulation Lecture 26: X Learning Lecture 27: Advanced Topics |
Week 10 | Discreteness in Generative Models | Lecture 28-29: Discreteness in Generative Models - Discrete Sequence Generation.pdf Lecture 29-30 Discreteness in Generative Models - Generating Graphs.pdf |
Practices
Week 11 | Paper Reading | Score-based Generative Model Graph Generation Image Super-resolution with Deep Generative Models Music Generation Models Controled Text Generation Graph Generative Model Code Generation StyleGAN |
Week 12 | Paper Reading | |
Week 13 | Paper Reading | |
Week 14 | Group Projects | |
Week 15 | Group Projects | |
Week 16 | Group Projects |
Course Staff
Feedback
For questions, please discuss on the Wechat group. You can also email Dr. Dong at hao.dong@pku.edu.cn.