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[D] Per channel or per sample Loss calculation and averaging in a batch ?

Let’s say we have an N-class semantic segmentation problem. Now on each iteration (for each batch) we can calculate Dice loss in two ways: (1) calculate average loss over classes for each sample in a batch and after that get the average over batch, or (2) calculate average loss per class in a batch and then average over classes presented in a batch. Which one is better and why? Or there is no difference at all? Can it affect on how model learns to segment small or big objects? Any related articles?

submitted by /u/AdelSexy
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.