# Why normalize data to the range [0,1] in autoencoders?

When people use autoencoders, they usually normalize the data such that the values are normalized to the range [0,1]. Why is that? Why not use zero-mean unit variance normalization for example? I read on a Quora answer that this range gives you more choice of loss functions, but I don't really understand why. Any ideas?

• Most likely the context here is autoencoders on images. With RGB, you have 256 colors for each channel, hence the input and output are bounded for each pixel, and therefore equivalent to $[0,1]$ after normalization. – Alex R. Sep 27 '17 at 19:32
• In that paper on biorxiv.org/content/early/2017/08/11/174474, they normalize the RNA-Seq levels to [0,1] as well while they are applying VAE on the gene expression data -- so, no images there. – user5054 Sep 27 '17 at 19:37