Prerequisites: Deep Learning, math, Python

Level: Advanced

What You'll Learn

• 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.