# Denoising Autoencoder Parameter Search

I have ran a hyperparameter search for a denoising autoencoder and the results suggest I should make the sizes of my hidden layers as large as possible (within the range of values I allowed it to search over.) This makes sense intuitively (i think?), wider layers would allow it to more easily copy the output to the input. However, I thought this trivial result was something a denoising autoencoder was meant to prevent.

Can anyone offer any advice? Thanks.

This is formalized as a transformation from $$\mathbb{R}^n$$ to $$\mathbb{R}^m$$ where $$m.