let’s say weights are as good as before  , as I’m not sure how such weights affect local bias with deep networks…
note that target tranformation has the same effect as adding an inverse transformation as activation function after the output layer - i.e. exp() and other smooth monotonic functions with positive outputs.
basically, you’re modulating gradient magnitudes per sample; custom loss, weights and transformations are just different (possibly complementary) methods for that.