Tnx for the response! There is a big difference, I plot a graph that indicates the second loss part effect and it is much better without the padding and the reconstruction loss also is worse… For example, I also tried to do the same but with padding, ifft before the forward, and remove padding and ftt after the forward so that they aren’t part of the backward, only encoder, adding noise and then decoder in the forward, I had to play with the network parameters since in this case there are much more neurons at the input but results were reasonable… I don’t see why should the padding as I presented above would change the results, I only added zeros from the sides, ifft, add noise, fft, and remove the zeros before the decoder, normalized as well so that variance is the same with and without padding. I am afraid that something goes wrong in the backward process, I don’t know what else can I do. I can put graphs to show the difference if that helps, will prepare them. Thank you!