CS 295: Deep Generative Models

Prof. Stephan Mandt
Spring 2020
Day/Time: Monday and Wednesday 11:00-12:20pm
Location: online lecture via Zoom.
Course Code: 34870

Attention: the main course page with all relevant and updated information is on Canvas (accessible with your UCI credentials).

Course Summary

Generative models are an important class of machine learning models that aim to learn the data distribution. Deep generative models build on recent advances in the fields of deep learning, and make it possible to sample data that highly resemble the structure of the data on which these models were trained. Recent success stories of deep generative models include Google’s WaveNet which set a new state of the art for voice synthesis, Transformer Networks for highly accurate machine translation, CycleGAN for weakly-supervised style transfer between images or videos, neural compression algorithms that outperform their classical counterparts, and deep generative models for molecular design. This course will introduce students to the probabilistic foundations of deep generative models with an emphasis on variational autoencoders (VAEs), generative adversarial networks (GANs), autoregressive models, and normalizing flows. Advanced topics that will be covered include black-box variational inference, variational dropout, disentangled representations, deep sequential models, alternative variational bounds, and information theoretical perspectives on VAEs. We will discuss diverse applications from the domains of computer vision, speech, NLP, and compression.


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Course Project

As part of the course, students will work on a research project in small groups. This project may deal with topics such as applications of deep generative models to a new domain or dataset, or improvements on the inference of deep generative models.

Suggested Reading

There is no book on the course topic. For deep learning, the textbook by Goodfellow, Bengio, and Courville is recommended (free online version). Links to relevant research papers will be provided.

Tentative Content

  • Introduction
  • Generative Models and Variational Inference
  • Autoregressive Models
  • Variational Autoencoders
  • Normalizing Flows
  • Generative Adversarial Networks
  • Structured Generative Models
  • Discrete Latent Variables in Deep Models
  • Information Theoretic Perspectives
  • Deep Sequential Models
  • Structured VAEs and Image/Video Compression