添加看似无信息的标签有助于低数据状态
Adding Seemingly Uninformative Labels Helps in Low Data Regimes
摘要：
有证据表明，在大型数据集上训练的网络泛化能力很好，这不仅是因为有大量的训练样本，而且还因为类的多样性，这鼓励了对丰富特征的学习。这就提出了一个问题，即当数据稀缺时，这是否仍然正确-在低数据状态下使用额外的标签学习是否有优势？在这项工作中，我们考虑一个任务，需要难以获得专家注释：乳腺X射线摄影图像中的肿瘤分割。我们表明，在低数据设置，性能可以得到改善，通过补充专家注释与看似没有信息的标签从非专家注释，把任务变成一个多类的问题。我们发现，这些收益增加时，较少的专家数据，并通过进一步的研究发现几个有趣的属性。我们在CSAW-S（我们在这里介绍的一个新数据集）上展示了我们的发现，并在两个公共数据集上证实了这些发现。
英文摘要：
Evidence suggests that networks trained on large datasets generalize well not solely because of the numerous training examples, but also class diversity which encourages learning of enriched features. This raises the question of whether this remains true when data is scarce - is there an advantage to learning with additional labels in low-data regimes? In this work, we consider a task that requires difficult-to-obtain expert annotations: tumor segmentation in mammography images. We show that, in low-data settings, performance can be improved by complementing the expert annotations with seemingly uninformative labels from non-expert annotators, turning the task into a multi-class problem. We reveal that these gains increase when less expert data is available, and uncover several interesting properties through further studies. We demonstrate our findings on CSAW-S, a new dataset that we introduce here, and confirm them on two public datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
