The course is devoted to modern generative models (mostly in the application to computer vision).
We will study the following types of generative models:
- autoregressive models,
- latent variable models,
- normalization flow models,
- adversarial models,
- diffusion and score models.
Special attention is paid to the properties of various classes of generative models, their interrelationships, theoretical prerequisites and methods of quality assessment.
The aim of the course is to introduce the student to widely used advanced methods of deep learning.
The course is accompanied by practical tasks that allow you to understand the principles of the considered models.
- telegram: @roman_isachenko
- e-mail: roman.isachenko@phystech.edu
| # | Date | Description | Slides |
|---|---|---|---|
| 1 | February, 20 | Lecture 1: Logistics. Generative models overview and motivation. Problem statement. Divergence minimization framework. Autoregressive models (ImageGPT). | slides |
| Seminar 1: Introduction. Maximum likelihood estimation. Histograms. Bayes theorem. PixelCNN. VAR. | slides | ||
| 2 | February, 27 | Lecture 2: Normalizing Flow (NF) intuition and definition. Linear NF. Gaussian autoregressive NF. Coupling layer (RealNVP). | slides |
| Seminar 2: Planar and Radial Flows. Forward vs Reverse KL. | slides | ||
| 3 | March, 6 | Lecture 3: Forward and reverse KL divergence for NF. Latent variable models (LVM). Variational lower bound (ELBO). EM-algorithm. | slides |
| Seminar 3: Forward vs Reverse KL. RealNVP | slides |
| Homework | Date | Deadline | Description | Link |
|---|---|---|---|---|
| 1 | February, 28 | March, 14 |
|
  |
- 6 homeworks each of 15 points = 90 points
- oral cozy exam = 30 points
- maximum points: 90 + 30 = 120 points
- probability theory + statistics
- machine learning + basics of deep learning
- python + pytorch