论文标题
M2M：通过主要翻译的分类不平衡分类
M2m: Imbalanced Classification via Major-to-minor Translation
论文作者
论文摘要
在大多数实际情况下，标记的培训数据集是高度的级别不平衡，深层神经网络遭受了概括到平衡的测试标准。在本文中，我们通过翻译更频繁的课程的样本（例如图像）来增强较少频繁的课程来探讨一种新颖而简单的方法来减轻此问题。这种简单的方法使分类器能够通过传输和利用多数信息的多样性来学习少数群体的更广泛的特征。我们对各种类不平衡数据集的实验结果表明，与其他现有的重新采样或重新加权方法相比，所提出的方法显着改善了对少数群体的概括。我们方法的性能甚至超过了以前最先进的分类方法的性能。
In most real-world scenarios, labeled training datasets are highly class-imbalanced, where deep neural networks suffer from generalizing to a balanced testing criterion. In this paper, we explore a novel yet simple way to alleviate this issue by augmenting less-frequent classes via translating samples (e.g., images) from more-frequent classes. This simple approach enables a classifier to learn more generalizable features of minority classes, by transferring and leveraging the diversity of the majority information. Our experimental results on a variety of class-imbalanced datasets show that the proposed method improves the generalization on minority classes significantly compared to other existing re-sampling or re-weighting methods. The performance of our method even surpasses those of previous state-of-the-art methods for the imbalanced classification.
