This course provides a comprehensive treatment of Variational Autoencoders (VAEs) and their application to representation learning. It covers advanced VAE architectures, sophisticated inference techniques, the pursuit of disentangled representations, and their extension to complex data types. Designed for AI engineers and researchers, this course emphasizes both the theoretical underpinnings and practical implementation of cutting-edge VAE models.
Prerequisites Deep Learning, math, Python
Level:
Advanced VAE Architectures
Implement and critically evaluate a range of advanced Variational Autoencoder architectures, including CVAEs, VQ-VAEs, and Hierarchical VAEs.
Sophisticated Inference Methods
Apply and analyze advanced variational inference techniques such as IWAEs and methods for structured posteriors to improve VAE performance.
Disentangled Representation Learning
Develop VAE models for learning disentangled representations and assess their quality using established metrics and theoretical frameworks.
Mathematical Foundations of VAEs
Attain a profound understanding of the mathematical principles governing VAEs, including the ELBO, reparameterization, and KL divergence.
VAEs for Complex Data
Adapt and implement VAEs for modeling sequential, structured, and high-dimensional data, such as text, graphs, and images.
Hybrid Generative Models
Analyze and construct hybrid models that combine VAEs with other generative techniques, like VAE-GANs, for enhanced capabilities.