研究人员开发了一个名为CARE的新框架，用于改进在具有不平衡类别分布和嘈杂标签的数据集上训练的机器学习模型。该方法利用视觉-语言模型的洞察力来适应性地纠正错误，对频率较低的类别应用更严格的纠正，对常见类别应用更宽松的纠正。实验表明，CARE比现有技术可以提高高达3.0%的性能。 AI
影响 增强了模型在真实世界数据集上的鲁棒性，有可能提高在数据分布倾斜的应用中的性能。
排序理由 该集群包含一篇详细介绍新机器学习框架的学术论文。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2605.23254v1 Announce Type: new Abstract: Learning from real-world data is frequently hindered by the compound challenge of long-tailed class distributions and noisy annotations. Existing methods partially address these issues but typically ignore the non-uniform impact of …
Learning from real-world data is frequently hindered by the compound challenge of long-tailed class distributions and noisy annotations. Existing methods partially address these issues but typically ignore the non-uniform impact of label noise across classes, resulting in ineffec…