基于标签层次的不变性学习
Invariance Learning based on Label Hierarchy
摘要：
深度神经网络继承了嵌入在训练数据中的虚假相关性，因此可能无法预测看不见的域（或环境）上的期望标签，这些域具有与训练中使用的域不同的分布。最近开发了不变性学习（IL）来克服这一缺点;使用许多领域的训练数据，IL估计这样一个预测器，该预测器对域的变化是不变的。然而，在多个领域的训练数据的要求是一个强大的限制IL，因为它往往需要高的注释成本。我们提出了一个新的IL框架来克服这个问题。假设来自多个域的数据对于更高级别的分类任务的可用性，对于该更高级别的分类任务，标记成本较低，我们使用单个域中的训练数据来估计目标分类任务的不变预测器。此外，我们提出了两种交叉验证方法来选择超参数的不变正则化，以解决超参数选择的问题，这在现有的IL方法没有得到妥善处理。实验证明了该框架的有效性，包括交叉验证，并在一定条件下证明了超参数选择的正确性。
英文摘要：
Deep Neural Networks inherit spurious correlations embedded in training data and hence may fail to predict desired labels on unseen domains (or environments), which have different distributions from the domain used in training. Invariance Learning (IL) has been developed recently to overcome this shortcoming; using training data in many domains, IL estimates such a predictor that is invariant to a change of domain. However, the requirement of training data in multiple domains is a strong restriction of IL, since it often needs high annotation cost. We propose a novel IL framework to overcome this problem. Assuming the availability of data from multiple domains for a higher level of classification task, for which the labeling cost is low, we estimate an invariant predictor for the target classification task with training data in a single domain. Additionally, we propose two cross-validation methods for selecting hyperparameters of invariance regularization to solve the issue of hyperparameter selection, which has not been handled properly in existing IL methods. The effectiveness of the proposed framework, including the cross-validation, is demonstrated empirically, and the correctness of the hyperparameter selection is proved under some conditions.
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。
