两篇新的研究论文介绍了一种用于识别和纠正机器学习数据集中损坏标签的框架。CANOLA 和 Relabeler 都旨在通过精炼噪声数据来提高模型性能，其中 CANOLA 专注于噪声感知学习和迭代软标签精炼，而 Relabeler 使用局部和全局数据关系进行检测和纠正。两种方法在实验中都显示出比现有技术显著的改进，从而提高了下游任务的性能。 AI
影响 这些框架提高的数据质量可能带来更强大、更准确的各种应用中的人工智能模型。
排序理由 两篇在 arXiv 上发表的学术论文介绍了纠正机器学习数据集中损坏标签的新方法。
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arXiv:2606.11695v1 Announce Type: cross Abstract: High-quality labeled data is essential for training reliable ML/DL models. However, real-world datasets often contain a considerable proportion of corrupted labels, which can severely degrade model performance. To address this pro…
arXiv:2606.11699v1 Announce Type: new Abstract: The performance of machine learning and deep learning models largely depends on the quality of the training data. However, the quality of the real-world datasets is often compromised by noisy labels, which can substantially degrade …