Comments (2)
I understand it that preloss is adaptaion loss about label_a(Number of classes per task) and postloss is meta-learning loss about lable_b(Number of test example per class).
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I understand it that preloss is adaptaion loss about label_a(Number of classes per task) and postloss is meta-learning loss about lable_b(Number of test example per class).
Hello, I have a question. Why does postoss get higher and higher in training? I think that should mean the accuracy.
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