论文标题
通过单方面对齐方式进行故障诊断的缺失类固定域的适应
Missing-Class-Robust Domain Adaptation by Unilateral Alignment for Fault Diagnosis
论文作者
论文摘要
域的适应性旨在通过利用源域中的学习知识并将其转移到目标域来提高模型性能。最近，在减轻源和目标域之间的分布变化方面，域对抗方法特别成功。但是，这些方法假设两个域之间的标签空间相同。由于目标训练集可能不包含完整的类别集，因此该假设对实际应用有一个重要的限制。我们在本文中证明，在训练过程中，域对抗方法的性能可能容易受到目标标签不完整的标签。为了克服这个问题，我们提出了一种两阶段的单方面对准方法。所提出的方法利用了源域的类间关系，并单方面与源域的目标对齐。首先在MNIST $ \ rightarrow $ MNIST-M改编任务上评估拟议方法的好处。还在故障诊断任务上评估了所提出的方法，在实践中，目标训练数据集中缺少故障类型的问题。这两个实验都证明了所提出的方法的有效性。
Domain adaptation aims at improving model performance by leveraging the learned knowledge in the source domain and transferring it to the target domain. Recently, domain adversarial methods have been particularly successful in alleviating the distribution shift between the source and the target domains. However, these methods assume an identical label space between the two domains. This assumption imposes a significant limitation for real applications since the target training set may not contain the complete set of classes. We demonstrate in this paper that the performance of domain adversarial methods can be vulnerable to an incomplete target label space during training. To overcome this issue, we propose a two-stage unilateral alignment approach. The proposed methodology makes use of the inter-class relationships of the source domain and aligns unilaterally the target to the source domain. The benefits of the proposed methodology are first evaluated on the MNIST$\rightarrow$MNIST-M adaptation task. The proposed methodology is also evaluated on a fault diagnosis task, where the problem of missing fault types in the target training dataset is common in practice. Both experiments demonstrate the effectiveness of the proposed methodology.
