Reduced focal loss implementation is incorrect for binary case #102
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Fixed Binary focal loss when reduced threshold is not None. Focal term should be continuous and equal to 1 at pt equal to reduced threshold
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Hmmm, indeed! Thanks for spotting this bug. I've never get any decent results with reduced focal loss, and this may explain why :)
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The current implementation is same as the one given in original paper . Although, it doesn't work as intended when value of reduced threshold is not 0.5. As per the paper, the focal term should be 1 when pt is less than reduced threshold and then exponentially decrease from 1 to 0 as pt goes from reduced threshold to 1. By this logic, the value of focal term should be 1 at pt equal to reduced threshold from both sides of the curve. From RHS the value is given as (1-pt/thresh) to power gamma multiplied by log pt. This will only be 1 at pt=thresh, when thresh=0.5, otherwise not.
So the correct formula focal term for pt>thresh should be ((1-pt)/(1-thresh) to the power gamma multiplied by log pt.
The corrected implementation goes hand in hand with the one done for softmax_focal_loss_with_logits which works as intended in the paper.