KFrank:

This network has the reflection symmetry “built in,” makes full use of all
of the information in the original (not-necessarily-symmetric) 1024x1024
input images, and predicts what can be understood as fully-symmetric
segmentation masks (which you could “unfold” back out to be full 1024x1024
symmetric masks if you wanted to for some reason).
If you can’t go this route, and want to use the loss function to encourage
symmetry of your predictions, you could add an “asymmetry penalty” to
your loss. Continuing with the reflection-symmetry example, take your
predicted asymmetrical, 1024x1024 mask, and calculate, say, the
intersection-over-union loss of the flipped 512x1024 left half versus the
unflipped 512x1024 right half. Add this penalty loss (with an adjustable
multiplicative weight) to whatever regular training loss you are using,
and train with this combined loss. As you make your penalty weight


So I can’t use the first approach because I have different spatial input sizes to the network.
Regarding the second approach, I have symmetry along the diagonal. But the example you gave was spot on.
Thanks