Long-tailed classifiers can be rebalanced after training by dividing class probabilities by the model's own average predicted prior, yielding small accuracy gains over using class frequencies.
A systematic study of the class imbalance problem in convo- lutional neural networks
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Prior2Posterior: Model Prior Correction for Long-Tailed Learning
Long-tailed classifiers can be rebalanced after training by dividing class probabilities by the model's own average predicted prior, yielding small accuracy gains over using class frequencies.