Class-Balanced Loss Based on Effective Number of Samples

Understand

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 on the number of observations for each class.
• In this work, we argue that as the number of samples increases, the additional benefit of a newly added data point will diminish.
• We introduce a novel theoretical framework to measure data overlap by associating with each sample a small neighboring region rather than a single point.