How to implement Siamese network with the two subnetworks not share weights
5 views (last 30 days)
I was implementing a Siamese using matlab deep learning toolbox. It is easy to implement such a network when the two subnetworks of the Siamese network share weights follwoing this official demo. Now I want to implement a Siamese network with the two subnetworks not share weights. Is there any easy solutions? I know we can set two "dlnetwork", one for input image A and the other for input image B. But the problem is you need to load two subnetworks into GPU memory, which is unavailable when there is no enough memory.
Any good solutions is welcomed, thank you!