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


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


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

For questions, please discuss on the Wechat group. You can also email Dr. Dong at hao.dong@pku.edu.cn.