I have my own data with the following dimensions for super resolution tasks.
(Batch, channels, 128, 128, 128).
ex) predict = (2, 1, 128, 128, 128) / output = (2, 1, 128, 128, 128)
torch.nn.MSELoss() or torch.nn.L1Loss() can be directly applied in this voxel case?
criterion = torch.nn.MSELoss() pred = torch.randn(2,1,128,128,128) y = torch.randn(2,1,128,128,128) loss = criterion(pred, y)
Or should I make other versions of losses for 3D voxels?
+) can anybody let me know if there is any strategy how to utilize SSIM loss with 3D voxels?