论文标题
学习不平衡数据集，最大保证金损失
Learning Imbalanced Datasets with Maximum Margin Loss
论文作者
论文摘要
提出了一种学习算法，称为最大利润率（MM），以考虑班级不平衡数据学习问题：训练有素的模型倾向于预测大多数班级而不是少数群体。也就是说，对于少数群体来说，不足似乎是概括的挑战之一。为了对少数群体进行良好的概括，我们设计了一个新的最大利润率（MM）损失函数，通过最大程度地减少通过转移决策结合的基于利润的概括。理论上原理的标签 - 分布式利润率（LDAM）损失已成功应用于以前的策略，例如重新降采用或重新采样以及有效的培训时间表。但是，他们尚未研究最大保证金损耗函数。在这项研究中，我们研究了两种类型的基于硬利润的决策边界的性能，其中LDAM对人为不平衡的CIFAR-10/100的培训时间表，以进行公平的比较和有效性。
A learning algorithm referred to as Maximum Margin (MM) is proposed for considering the class-imbalance data learning issue: the trained model tends to predict the majority of classes rather than the minority ones. That is, underfitting for minority classes seems to be one of the challenges of generalization. For a good generalization of the minority classes, we design a new Maximum Margin (MM) loss function, motivated by minimizing a margin-based generalization bound through the shifting decision bound. The theoretically-principled label-distribution-aware margin (LDAM) loss was successfully applied with prior strategies such as re-weighting or re-sampling along with the effective training schedule. However, they did not investigate the maximum margin loss function yet. In this study, we investigate the performances of two types of hard maximum margin-based decision boundary shift with LDAM's training schedule on artificially imbalanced CIFAR-10/100 for fair comparisons and effectiveness.
