• 深度生成模型
• • 基础
  • 研究 & 应用

深度生成模型

北京大学 董豪 hao.dong@pku.edu.cn

基础

Week 1	Introduction	Lecture 1: Introduction Lecture 2: Data Representation Lecture 3: Mathematic Foundation & Basic Concept
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: Normalising Flow Background Lecture 11-12: Normalising Flow Models
Week 5	Generative Adversarial Networks	Lecture 13: Introduction of GAN Lecture 14: Understanding GAN Lecture 15: Selected GANs
Week 6	Practice	Lecture 16-18: Practice: VAE and GAN Lecture 16-18: Demo Code

研究 & 应用

Week 7	Evaluation of Generative Models	Lecture 19: Sampling Quality Lecture 20: Density Evaluation & Latent Representation Lecture 21: Practice
Week 8	Energy-based Models	Lecture 22: Hopfield Network Lecture 23: Boltzmann Machine Lecture 24: Energy-based GANs
Week 9	Challenges of Generative Models	Lecture 25: High-dimensional Data Generation Lecture 26: Learning Large Encoder Lecture 27: Other Challenges
Week 10	Applications of Generative Models	Lecture 28: Image Synthesis, Translation and Manipulation Lecture 29: X Learning Lecture 30: Advanced Topics