论文标题
Gradtail：使用基于梯度的样本加权学习长尾数据
GradTail: Learning Long-Tailed Data Using Gradient-based Sample Weighting
论文作者
论文摘要
我们提出了gradtail，这是一种使用梯度在面对长尾训练数据分布的过程中飞跃地改善模型性能的算法。与传统的长尾分类器不同，该分类器在融合（可能是过度合适的模型）上运行，我们证明了一种基于梯度点点产品一致性的方法可以在模型训练期间早期隔离长尾数据，并通过动态选择该数据的较高样品权重来提高性能。我们表明，这种上升重量导致分类和回归模型的模型改进，后者在长尾文献中相对尚未探索，并且梯度对准发现的长尾示例与我们的语义期望一致。
We propose GradTail, an algorithm that uses gradients to improve model performance on the fly in the face of long-tailed training data distributions. Unlike conventional long-tail classifiers which operate on converged - and possibly overfit - models, we demonstrate that an approach based on gradient dot product agreement can isolate long-tailed data early on during model training and improve performance by dynamically picking higher sample weights for that data. We show that such upweighting leads to model improvements for both classification and regression models, the latter of which are relatively unexplored in the long-tail literature, and that the long-tail examples found by gradient alignment are consistent with our semantic expectations.
