Hello, I’m no expert about 1d-convolutions but let’s give it a go.
For dealing with image data there has been a shift away from using fully connected layers and replace them with conv layers. One reason for this is that the conv has spatial awareness the fully connected layers lack.
The same goes for one dimension. If you use 1d-deconv you keep the spatial awareness throughout the network. If you use fully connected layers these connections get influenced from everywhere and I’m guessing it could confuse the network. In essence, I believe it’s easier to fuck up -> harder to train.
Would love to hear back if you find some other explanation