Methods in long-tail learning focus on improving performance for data-poor (rare) classes; however, performance for such classes remains much lower than performance for more data-rich (frequent) classes. Analyzing the predictions of long-tail methods for rare classes reveals that a large number of errors are due to misclassification of rare items as visually similar frequent classes. To address this problem, we introduce AlphaNet, a method that can be applied to existing models, performing post hoc correction on classifiers of rare classes. Starting with a pre-trained model, we find frequent classes that are closest to rare classes in the model's representation space and learn weights to update rare class classifiers with a linear combination of frequent class classifiers. AlphaNet, applied to several models, greatly improves test accuracy for rare classes in multiple long-tailed datasets, with very little change to overall accuracy. Our method also provides a way to control the trade-off between rare class and overall accuracy, making it practical for long-tail classification in the wild.

翻译：长尾学习方法旨在提升数据稀疏（稀有类）的性能，然而这类类别的准确率仍显著低于数据丰富（常见类）的类别。分析长尾方法对稀有类的预测结果发现，大量错误源于将稀有项误分类为视觉相似的常见类。为解决这一问题，我们提出AlphaNet，一种可应用于现有模型的方法，对稀有类的分类器进行事后校正。基于预训练模型，我们找出在模型表示空间中与稀有类最接近的常见类，并学习权重以通过常见类分类器的线性组合更新稀有类分类器。将AlphaNet应用于多个模型后，在多个长尾数据集中显著提升了稀有类的测试准确率，同时整体准确率变化极小。该方法还能调控稀有类准确率与整体准确率之间的权衡，使其适用于实际场景中的长尾分类任务。