Hello everyone,
Let’s say I have data with shape N, and an Autoencoder that takes as input this type of data that are normalized, so the general steps are:
1- Data lies in the original domain
2- Data is normalized
3- Data passes through the Autoencoder
4- Reconstructed data lies in the normalized domain
5- Reconstructed data is “denormalized” in order to lie in the original domain.
The thing is I need to stay in the normalized domain during training. So, the loss is computed in the normalized domain: the Autoencoder learns to reconstruct the data with equal weights to the N values. But some of those N values are more important than other when lying in the original domain, so the Autoencoder should learn to reconstruct the data with different weights.
So my question is: how can I learn those weights?
Is it possible to make a “double” learning that would go this way:

initialize two neural networks: an Autoencoder and another one with learnable parameters (the weights)
train the Autoencoder. The loss is computed with weights from the other network
when validating (validation is done in the original domain), use the results in the original domain to adjust the parameters corresponding to the weights

Or is there a simpler way of doing this?
I can’t find examples of code doing those kind of things. Does someone have some resources?
Thanks a lot for reading