Chapter 08.02: Autoencoders
This subchapter covers the task and structure of AEs, which compress data into a lower-dimensional latent space and reconstruct it. In addition, we focus on undercomplete AEs, enforcing a “bottleneck” to focus on essential features. Linear undercomplete AEs with L2-reconstruction error approximate PCA by identifying principal components, while nonlinear AEs extend this capability to capture complex data patterns.