Previous chapters established the core concepts behind autoencoders, including classic structures, regularization methods, and the generative capabilities of Variational Autoencoders (VAEs). While these form a solid foundation, effectively handling specific data types like images or sequences, or achieving particular latent space characteristics, often requires architectures adapted to the task.

This chapter examines several such advanced autoencoder designs. We will cover:

• Convolutional Autoencoders (CAEs) optimized for spatial hierarchies in data like images.
• Recurrent Autoencoders (RAEs) suited for sequential inputs such as time series or text.
• Adversarial Autoencoders (AAEs) which utilize adversarial training to impose a desired prior distribution on the latent space.
• Vector Quantized Variational Autoencoders (VQ-VAEs) that operate with discrete, rather than continuous, latent spaces using codebooks.
• An overview of Transformer-based autoencoding approaches relevant to modern sequence processing.

For each architecture, we will discuss its construction, the reasoning behind its design modifications compared to simpler autoencoders, and its common application areas. Practical implementation details for select architectures will also be addressed.