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Found 2 papers, 1 papers with code
A two-head loss function for deep Average-K classification
Average-K classification is an alternative to top-K classification in which the number of labels returned varies with the ambiguity of the input image but must average to K over all the samples.
Stochastic smoothing of the top-K calibrated hinge loss for deep imbalanced classification
In modern classification tasks, the number of labels is getting larger and larger, as is the size of the datasets encountered in practice.
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