Generative AI for Data Compression and Transmission

Aim

After completing the course, the student should be able to:

• explain theoretical foundations of deep generative modeling, including methods for modeling a density function, network structure, loss function, and training routines;
• implement and train generative AI models to generate new data, and apply these models in a data compression and transmission system;
• analyze and evaluate the effectiveness of a solution based on generative modeling for data compression and transmission;
• reflect on implications and ethical considerations of using generative AI tools

Outcomes 1, 3, and 4 are fostered by the lectures and tutorieals, and assessed via the written examination. Outcomes 2 and 3 are fostered by lab sessions, and assessed via the lab reports. Outcome 4 is further fostered by peer assessment

Content

This course provides an introduction to generative models, their applications to data compression and transmission, and their implications and ethical considerations. This includes: introduction to probabilistic generative modeling; generative models (including variational autoencoders, generative adversarial networks, diffusion models, flow-based models, energy-based models, transformers); frameworks and techniques for generative-AI-based data compression and transmission, including neural compression, rate-distortion-perception tradeoff, latent coding, joint source-channel coding; privacy, security, and ethical considerations of generative AI.