论文标题
TOP-K校准的铰链损失的随机平滑，以进行深度不平衡分类
Stochastic smoothing of the top-K calibrated hinge loss for deep imbalanced classification
论文作者
论文摘要
在现代分类任务中，标签的数量越来越大，就像在实践中遇到的数据集的大小一样。随着班级数量的增加，阶级的歧义和阶级失衡变得越来越有问题，以达到高顶级1的准确性。同时，TOP-K指标（允许K猜测的指标）变得流行，尤其是在性能报告中。然而，提出为深度学习量身定制的顶级K损失仍然是一个挑战，无论是理论上还是实际的。在本文中，我们引入了由TOP-K校准损失的最新发展启发的随机TOP-K铰链损失。我们的建议基于在灵活的“扰动优化器”框架上的Top-K操作员建筑的平滑基础。我们表明，在平衡数据集的情况下，我们的损失函数的性能非常出色，同时，与最先进的TOP-K损失函数相比，计算时间明显低。此外，我们为不平衡案例提出了简单的损失变体。在重尾数据集上的实验表明，我们的损失函数显着优于其他基线损失功能。
In modern classification tasks, the number of labels is getting larger and larger, as is the size of the datasets encountered in practice. As the number of classes increases, class ambiguity and class imbalance become more and more problematic to achieve high top-1 accuracy. Meanwhile, Top-K metrics (metrics allowing K guesses) have become popular, especially for performance reporting. Yet, proposing top-K losses tailored for deep learning remains a challenge, both theoretically and practically. In this paper we introduce a stochastic top-K hinge loss inspired by recent developments on top-K calibrated losses. Our proposal is based on the smoothing of the top-K operator building on the flexible "perturbed optimizer" framework. We show that our loss function performs very well in the case of balanced datasets, while benefiting from a significantly lower computational time than the state-of-the-art top-K loss function. In addition, we propose a simple variant of our loss for the imbalanced case. Experiments on a heavy-tailed dataset show that our loss function significantly outperforms other baseline loss functions.
