Hi,
Thanks for your suggestions.
The dropout layer is a great idea, it’s especially great because I thought about it so now I know I’m not the only one thinking that way. I tried applying dropout to the second input and tried different dropout values (0.1-0.9) but it it didn’t help.
It’s also interesting to check the value range of the VDCNN vs the linear layer output (from the second input). I will check it today.
I also thought about a different solution - train networks seperately for the inputs and then connecting them in a bigger network by concatenating the outputs and feeding them to a linear layer, while training only the last layer.