I’ve created a variational autoencoder to encode 1-dimensional arrays. The encoding is done through 3 1d-convolutional layers. Then, after the sampling trick, I reconstruct the series using 3 fully connected layers. Here are my questions in case some can shed some light on it:
I think it would be better if I use 1d-deconvolutional layers instead of fully connected, but I cannot understand precisely why.

It’s because it would bring better results? But then, if the FC layer is complex enough should be able to archive the same results, right?
It’s because would be more efficient? This is, would get the same results as a complex enough FC layer but with less training and parameters?
Or it’s because of other reasons that I’m missing.

Thanks.