Cross Validated Asked by BMurray on March 4, 2021
I’ve been struggling with the implication of the dice loss value generated when t
(target label) and p
(predicted label) are entirely disjoint. Dice score being (2pt)/(p+t)
, this value is zero (or very close with epsilon being involved) for all disjoint but non-empty t
and p
. The derivative of the dice score is (2t^2)/((p+t)^2)
, which means that there is still gradient when t
and p
are disjoint, provided that t
is not empty. Does this mean that dice performs poorly from a training standpoint as a loss when t
and p
are disjoint or does it just mean that it isn’t informative as a metric (i.e. that dice loss returns 1 due to dice score returning 0 as its numerator is always zero when t
and p
are disjoint)?
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