Open aexposit opened 3 days ago
Personally I would favour flexibility. There are situations where you might want to save on CPU with lighter models where you don't need as many parameters. Or maybe you need a heavier model is some specific circumstances.
Hi while creating the neural network dynamically gives flexibility, at the end probably more than 95% of the NAM models are NAM "STANDARD" (the network with up to 512 dilation rates and 2 layers). Is there any better performance if just NAM "standard" is used in your code and the network model is fixed ? Just out of my curiosity...
Thx a lot