While standard autoencoders provide a foundation for learning compressed representations, they can sometimes learn trivial solutions or overfit the training data, limiting their effectiveness in capturing meaningful data structures. This chapter introduces regularization techniques designed to overcome these issues and encourage the learning of more stable features.

We will examine several key approaches. You'll learn about Sparse Autoencoders, which enforce sparsity in the latent code using penalties like $L_1$ regularization or Kullback–Leibler (KL) divergence, effectively making the network focus on the most salient information. We will also cover Denoising Autoencoders (DAEs), which are trained to reconstruct clean data from corrupted versions, thereby learning features resilient to input noise. Additionally, we'll discuss Contractive Autoencoders (CAEs), which explicitly penalize the sensitivity of the learned features to small input variations.

By understanding and implementing these methods, you will be able to build autoencoder models that generalize better and learn more useful representations for various tasks. We will compare these techniques and provide practical implementation guidance, including hands-on exercises for Denoising and Sparse Autoencoders.