FDD3601 Deep Generative Models and Synthesis 7.5 credits

Relevant concepts from probability theory and estimation Introduction to synthesis problems and generative models
Principles of synthesis versus classification
Regression versus probabilistic modelling
Modelling goals and evaluation
Mixture density networks (MDNs)
Autoregression and large language models (LLMs)
Normalising flows
Variational autoencoders (VAEs)
Diffusion models and flow matching
Generative adversarial networks (GANs)
Subjective evaluation
Hybrid approaches
Recent developments
Ethical aspects of generative AI

After passing the course, the students should be able to:
characterise synthesis problems, deep generative methods, and their applications
distinguish different objectives, performance measures, and common problems with generative modelling
describe the relation between deep generative models and regression-based methods
train and tune deep generative models on different datasets
evaluate generative models objectively and subjectively
discuss ethical aspects of particular relevance to generative AI
in order to
be able to judiciously use deep generative modelling to solve problems in industrv and/or academia.

• LAB1 - Laboratory work, 7.5 credits, grading scale: P, F

The final grade is determined by accumulating the grades in individual exercises.

Josephine Sullivan

Gustav Henter

Hossein Azizpour

• All members of a group are responsible for the group's work.
• In any assessment, every student shall honestly disclose any help received and sources used.
• In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.