Having established the foundational principles of basic autoencoders, we now consider several advanced architectures. These variations are designed to improve feature quality, encourage robustness, or instill specific desirable properties into the learned representations, going beyond what standard autoencoders can achieve.

This chapter will introduce you to:

• Sparse Autoencoders: Learn how these models induce sparsity in their hidden unit activations, often by adding a penalty term like an $L_1$ regularizer or a KL-divergence constraint to the loss function. This encourages the autoencoder to learn more specific and potentially disentangled features.
• Denoising Autoencoders: Understand models trained to reconstruct a clean input $x$ from a partially corrupted version $\tilde{x}$. This process forces the autoencoder to learn more robust features by capturing the underlying data manifold rather than simply learning an identity function.
• Contractive Autoencoders: Explore architectures that aim to make the learned features resistant to small variations in the input. This is achieved by adding a penalty term to the loss function that penalizes large derivatives of the encoder's output with respect to the input.
• Stacked Autoencoders: Discover how to build deeper autoencoder networks by stacking multiple autoencoder layers. These are often trained greedily, one layer at a time, to learn hierarchical feature representations of increasing complexity.

The chapter also includes a hands-on section where you will implement a Denoising Autoencoder to solidify these concepts.