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Class-balanced-loss-pytorch

Pytorch implementation of the paper Class-Balanced Loss Based on Effective Number of Samples presented at CVPR'19.

Yin Cui, Menglin Jia, Tsung-Yi Lin(Google Brain), Yang Song(Google), Serge Belongie

Dependencies

Review article of the paper

Medium Article

How it works

It works on the principle of calculating effective number of samples for all classes which is defined as:

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Thus, the loss function is defined as:

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Visualisation for effective number of samples

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References

official tensorflow implementation