Class-Balanced Loss Based on Effective Number of Samples

CVPR, Volume abs/1901.05555, 2019, Pages 9268-9277.

Cited by: 286|Views194
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Abstract:

With the rapid increase of large-scale, real-world datasets, it becomes critical to address the problem of long-tailed data distribution (i.e., a few classes account for most of the data, while most classes are under-represented). Existing solutions typically adopt class re-balancing strategies such as re-sampling and re-weighting based o...More

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