标题： 基数增强的损失函数 显示英文标题

标题： Cardinality augmented loss functions

摘要： 类别不平衡是神经网络训练中一个常见且有害的问题。 通常，不平衡的主要类别会在训练中占主导地位，从而使分类器性能偏向主要结果。 为了解决这个问题，我们引入了基数增强的损失函数，这些函数来源于现代数学文献中的类似基数的不变量，如度量和分布。 这些不变量通过评估度量空间的“有效多样性”来丰富基数的概念，因此代表了过度同质化训练数据的自然解决方案。 在本工作中，我们建立了一种在神经网络训练中应用基数增强损失函数的方法，并报告了在人工不平衡数据集以及一个现实世界中的不平衡材料科学数据集上的结果。 我们观察到少数类别有显著的性能提升，以及整体性能指标的提升。 显示英文摘要

摘要： Class imbalance is a common and pernicious issue for the training of neural networks. Often, an imbalanced majority class can dominate training to skew classifier performance towards the majority outcome. To address this problem we introduce cardinality augmented loss functions, derived from cardinality-like invariants in modern mathematics literature such as magnitude and the spread. These invariants enrich the concept of cardinality by evaluating the `effective diversity' of a metric space, and as such represent a natural solution to overly homogeneous training data. In this work, we establish a methodology for applying cardinality augmented loss functions in the training of neural networks and report results on both artificially imbalanced datasets as well as a real-world imbalanced material science dataset. We observe significant performance improvement among minority classes, as well as improvement in overall performance metrics.