# Missing-Class-Robust Domain Adaptation by Unilateral Alignment for Fault Diagnosis

arXiv: https://arxiv.org/abs/2001.02015 · HTML: https://mlsanity.com/paper/2001.02015/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2020.

## Built on by (most cited first, 11 of 11)

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- [Task Aligned Generative Meta-learning for Zero-shot Learning](https://mlsanity.com/paper/2103.02185/) (2021)
- [Controlled Generation of Unseen Faults for Partial and Open-Partial Domain Adaptation](https://mlsanity.com/paper/2204.14068/) (2022)
- [Enhancing ensemble learning and transfer learning in multimodal data analysis by adaptive dimensionality reduction](https://mlsanity.com/paper/2105.03682/) (2021)
- [Federated Learning with Uncertainty-Based Client Clustering for Fleet-Wide Fault Diagnosis](https://mlsanity.com/paper/2304.13275/) (2023)
- [Association Graph Learning for Multi-Task Classification with Category Shifts](https://mlsanity.com/paper/2210.04637/) (2022)
- [Smart filter aided domain adversarial neural network for fault diagnosis in noisy industrial scenarios](https://mlsanity.com/paper/2307.01429/) (2023)
- [An Intelligent Approach to Detecting Novel Fault Classes for Centrifugal Pumps Based on Deep CNNs and Unsupervised Methods](https://mlsanity.com/paper/2309.12765/) (2023)
- [SigWavNet: Learning Multiresolution Signal Wavelet Network for Speech Emotion Recognition](https://mlsanity.com/paper/2502.00310/) (2025)
- [DeFed-GMM-DaDiL: A Decentralized Federated Framework for Domain Adaptation](https://mlsanity.com/paper/2605.04324/) (2026)

## Abstract (arXiv)

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 MNISTrightarrowMNIST-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.