论文标题
通过选择性一致性正规化的时间序列分类的域概括
Domain Generalization via Selective Consistency Regularization for Time Series Classification
论文作者
论文摘要
域的概括方法旨在学习使用有限数量的源域的数据来学习强大的域转移，并且在训练过程中无需访问目标域样本。域概括的流行域对齐方法寻求通过最大程度地减少所有域中特征分布之间的差异，而无视域间关系来提取域不变特征。在本文中，我们提出了一种新颖的表示学习方法，该方法有选择地强制估计密切相关的源域之间的预测一致性。具体而言，我们假设域共享不同的类信息表示表示，因此，我们仅将与密切相关的域之间的差异正规化，而不是使所有可能导致负转移的域对齐。我们将我们的方法应用于时间序列分类任务，并在三个公共现实世界数据集上进行全面的实验。与最先进的方法相比，我们的方法在基准基线方面显着改善，并在准确性和模型校准方面取得更好或竞争性能。
Domain generalization methods aim to learn models robust to domain shift with data from a limited number of source domains and without access to target domain samples during training. Popular domain alignment methods for domain generalization seek to extract domain-invariant features by minimizing the discrepancy between feature distributions across all domains, disregarding inter-domain relationships. In this paper, we instead propose a novel representation learning methodology that selectively enforces prediction consistency between source domains estimated to be closely-related. Specifically, we hypothesize that domains share different class-informative representations, so instead of aligning all domains which can cause negative transfer, we only regularize the discrepancy between closely-related domains. We apply our method to time-series classification tasks and conduct comprehensive experiments on three public real-world datasets. Our method significantly improves over the baseline and achieves better or competitive performance in comparison with state-of-the-art methods in terms of both accuracy and model calibration.
