My task is MRI-based pseudo-CT generation.
I combine the SASA with the CycleGAN(https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix).
Considering the GPU memory usage I halved the number of feature maps.
I found that the network without SASA performs best.
When I add SASA to D, the results are really bad.
Did anyone manage to add SASA to CycleGAN?
Hoping for a reply, Thank you.
My task is MRI-based pseudo-CT generation.
I combine the SASA with the CycleGAN(https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix).
Considering the GPU memory usage I halved the number of feature maps.
I found that the network without SASA performs best.
When I add SASA to D, the results are really bad.
Did anyone manage to add SASA to CycleGAN?
Hoping for a reply, Thank you.