Missing-Class-Robust Domain Adaptation by Unilateral Alignment for Fault Diagnosis
This addresses a practical limitation in fault diagnosis and similar applications where target datasets may miss classes, making domain adaptation more robust for real-world scenarios.
The paper tackles the problem of domain adaptation when the target domain lacks some classes present in the source domain, showing that existing adversarial methods are vulnerable to this incomplete label space. It proposes a two-stage unilateral alignment method that uses source inter-class relationships to align the target to the source, demonstrating effectiveness on MNIST→MNIST-M and a fault diagnosis task.
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.