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. The effective number of samples is defined as the volume of samples and can be calculated by a simple formula @math , where @math is the number of samples and @math is a hyperparameter. We design a re-weighting scheme that uses the effective number of samples for each class to re-balance the loss, thereby yielding a class-balanced loss. Comprehensive experiments are conducted on artificially induced long-tailed CIFAR datasets and large-scale datasets including ImageNet and iNaturalist. Our results show that when trained with the proposed class-balanced loss, the network is able to achieve significant performance gains on long-tailed datasets.
@cite_56: A way of improving the efficiency of gland seals of a large steam turbine comprising injecting into the seals a sacrificial bleed of low-energy fluid. This reduces the energy leakage of the gland seals and thus increases overall power plant efficiency.
Our theoretical framework is inspired by the random covering problem @cite_56 , where the goal is to cover a large set by a sequence of i.i.d. random small sets. We simplify the problem in by making reasonable assumptions. Note that the effective number of samples proposed in this paper is different from the concept of effective sample size in statistics. The effective sample size is used to calculate variance when samples are correlated.