Model bias triggered by long-tailed data has been widely studied. However, measure based on the number of samples cannot explicate three phenomena simultaneously: (1) Given enough data, the classification performance gain is marginal with additional samples. (2) Classification performance decays precipitously as the number of training samples decreases when there is insufficient data. (3) Model trained on sample-balanced datasets still has different biases for different classes. In this work, we define and quantify the semantic scale of classes, which is used to measure the feature diversity of classes. It is exciting to find experimentally that there is a marginal effect of semantic scale, which perfectly describes the first two phenomena. Further, the quantitative measurement of semantic scale imbalance is proposed, which can accurately reflect model bias on multiple datasets, even on sample-balanced data, revealing a novel perspective for the study of class imbalance. Due to the prevalence of semantic scale imbalance, we propose semantic-scale-balanced learning, including a general loss improvement scheme and a dynamic re-weighting training framework that overcomes the challenge of calculating semantic scales in real-time during iterations. Comprehensive experiments show that dynamic semantic-scale-balanced learning consistently enables the model to perform superiorly on large-scale long-tailed and non-long-tailed natural and medical datasets, which is a good starting point for mitigating the prevalent but unnoticed model bias.

翻译：长尾数据引发的模型偏差已得到广泛研究。然而，基于样本数量的度量方法无法同时解释以下三个现象：(1) 在数据充足的情况下，增加额外样本带来的分类性能提升微乎其微；(2) 当数据不足时，分类性能会随着训练样本数量的减少而急速下降；(3) 在样本平衡数据集上训练的模型，对不同类别仍存在不同偏差。本文定义并量化了类别的语义尺度，用以衡量类别的特征多样性。实验发现语义尺度存在边际效应，这完美描述了前两个现象。进一步地，我们提出了语义尺度不平衡的量化度量方法，该方法能够准确反映多个数据集（甚至包括样本平衡数据）上的模型偏差，为类别不平衡研究提供了新视角。由于语义尺度不平衡的普遍存在，我们提出了语义尺度平衡学习，包括通用损失改进方案和动态重加权训练框架，克服了迭代过程中实时计算语义尺度的挑战。综合实验表明，动态语义尺度平衡学习能够持续使模型在大规模长尾和非长尾自然及医学数据集上表现优异，为缓解普遍存在但未被关注的模型偏差提供了良好起点。