see the demo image on the right and the end coder takes in the noisy image puts it into a smaller latent representation which is the features that we care about and the decoder is trying to use these features to get back the original input so you hope that the encoder gets the high level details removes the noise and the decoder can up sample that and get get you back to the actual image so depending on the kind of noise you add you've want to learn more non-trivial things if you don't add any noise you're just going to learn an identity function