Mar 1, 2021
Your argument is correct. However, in most anomaly detection models with autoencoder, an overcomplete AE is useful. An overcomplete AE has larger feature space than the input. Regularization is added to the AE to learn the relevant sparse information and use for anomaly detection. Chapter 7 in my book “Understanding Deep Learning” explains this in more detail.
