What is the role of regularization in autoencoders?

Regularization prevents autoencoders from simply memorizing input data.

Without constraints, autoencoders can overfit by learning identity mapping. Regularization techniques like sparsity penalties, dropout, denoising objectives, or KL divergence (in VAEs) force the model to learn meaningful compressed representations rather than memorization.