Class-Balanced Loss Based on Effective Number of Samples

TL;DR

This paper introduces a novel class-balanced loss function based on the effective number of samples, which improves performance on long-tailed datasets by re-weighting classes according to a new theoretical measure.

Contribution

The paper proposes a new theoretical framework for measuring data overlap and defines the effective number of samples to enhance class re-balancing in long-tailed datasets.

Findings

Significant performance improvements on long-tailed CIFAR datasets.

Effective number-based re-weighting outperforms traditional methods.

Successful application to large-scale datasets like ImageNet and iNaturalist.

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 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 $(1-\beta^{n})/(1-\beta)$, where $n$ is the number of samples and $\beta \in [0,1)$ 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.