Modern machine learning models are becoming increasingly expensive to train for real-world image and text classification tasks, where massive web-scale data is collected in a streaming fashion. To reduce the training cost, online batch selection techniques have been developed to choose the most informative datapoints. However, these techniques can suffer from poor worst-class generalization performance due to class imbalance and distributional shifts. This work introduces REDUCR, a robust and efficient data downsampling method that uses class priority reweighting. REDUCR reduces the training data while preserving worst-class generalization performance. REDUCR assigns priority weights to datapoints in a class-aware manner using an online learning algorithm. We demonstrate the data efficiency and robust performance of REDUCR on vision and text classification tasks. On web-scraped datasets with imbalanced class distributions, REDUCR significantly improves worst-class test accuracy (and average accuracy), surpassing state-of-the-art methods by around 15%.

翻译：现代机器学习模型在面向真实世界的图像与文本分类任务时，训练成本日益高昂，因为这些场景需要以流式方式收集海量网络规模数据。为降低训练成本，在线批次选择技术被开发出来，用于挑选信息量最丰富的数据点。然而，这些技术可能因类别不平衡和分布偏移而导致最差类别泛化性能不佳。本研究提出REDUCR——一种基于类别优先级重加权的鲁棒高效数据降采样方法。该方法在保留最差类别泛化性能的同时削减训练数据量，并通过在线学习算法以类别感知方式为数据点分配优先级权重。我们在视觉和文本分类任务上验证了REDUCR的数据效率与鲁棒性能。针对类别分布不平衡的网络抓取数据集，REDUCR显著提升了最差类别的测试准确率（以及平均准确率），比现有最优方法高出约15%。