Having covered the core principles, advanced architectures, and sophisticated inference methods for Variational Autoencoders (VAEs), this chapter broadens our scope to examine their extended applications and integrations with other machine learning approaches.

You will learn to:

• Apply VAEs in semi-supervised learning contexts.
• Utilize VAEs for anomaly and out-of-distribution detection.
• Understand the comparative strengths and weaknesses of VAEs versus Generative Adversarial Networks (GANs).
• Construct and analyze hybrid models, such as VAE-GANs and Adversarial Autoencoders (AAEs).
• Recognize the role of VAEs in model-based reinforcement learning.
• Study techniques, such as denoising VAEs, for making models less susceptible to input perturbations and improving feature learning.
• Consider advanced optimization strategies specifically for VAE training.

The chapter also incorporates practical exercises on implementing hybrid VAE-GAN models, enabling you to apply these extended concepts.