Image Segmentation - binary cross entropy indicates over-fitting, dice doesn't
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I encountered a problem which is difficult for me to explain:
I train a modified UNet variant with binary cross entropy and I plot the dice scores as well. This is how the result looks like. The distribution of the mask pixels in the training and the test set are different.
Can anyone elaborate why dice and BCE is not correlating at all in this case? Or what are potential reasons why the two are not correlating?
Thanks!
deep-learning
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up vote
-1
down vote
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I encountered a problem which is difficult for me to explain:
I train a modified UNet variant with binary cross entropy and I plot the dice scores as well. This is how the result looks like. The distribution of the mask pixels in the training and the test set are different.
Can anyone elaborate why dice and BCE is not correlating at all in this case? Or what are potential reasons why the two are not correlating?
Thanks!
deep-learning
add a comment |
up vote
-1
down vote
favorite
up vote
-1
down vote
favorite
I encountered a problem which is difficult for me to explain:
I train a modified UNet variant with binary cross entropy and I plot the dice scores as well. This is how the result looks like. The distribution of the mask pixels in the training and the test set are different.
Can anyone elaborate why dice and BCE is not correlating at all in this case? Or what are potential reasons why the two are not correlating?
Thanks!
deep-learning
I encountered a problem which is difficult for me to explain:
I train a modified UNet variant with binary cross entropy and I plot the dice scores as well. This is how the result looks like. The distribution of the mask pixels in the training and the test set are different.
Can anyone elaborate why dice and BCE is not correlating at all in this case? Or what are potential reasons why the two are not correlating?
Thanks!
deep-learning
deep-learning
asked Nov 11 at 14:54
Styp
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1398
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