The last sentence explained how the MSELoss can be interpreted.
I.e. I would claim you are free to scale the loss during training to make the model converge, but the loss value itself might not be meaningful. E.g. considering the previous example of the keypoint detection I would like to know how many pixels are my predicted keypoints away from the target keypoints. To do so, I would need to get predictions in the “pixel space”, i.e. the predictions should have values in the range [0, image_height] and [0, image_width] (same as the target). If I’ve scaled the predictions during training to e.g. a range of [0, 1] (for better convergence), I would need to unscale them in order to interpret the loss in my target domain (i.e. the pixel space or image coordinates).



 Chin_GM:

I saw some journal papers often publish the value of MSELoss, so I wonder the MSELoss is before unscale or after unscale.


I don’t know, but would assume the authors explain it in their paper.