Partially-Labeled Domain Generalization via Multi-Dimensional Domain Adaptation
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2023 International Joint Conference on Neural Networks (IJCNN), 2023, 2023-June, pp. 1-8
- Issue Date:
- 2023
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Partially-Labeled_Domain_Generalization_via_Multi-Dimensional_Domain_Adaptation (1).pdf | Published version | 798.43 kB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
Domain generalization deals with a challenging setting where several labeled source domains are given and the goal is to train machine learning models that can generalize to an unseen test domain However in practice labeled samples are often difficult and expensive to obtain Thus the source domains would not always be labeled When only some source domains are labeled and others are unlabeled we formally introduce this domain generalization problem as Partially Labeled Domain Generalization PLDG In this paper we study the most chal lenging setting in PLDG problems where only one source domain is labeled and a few unlabeled source domains are available To enable generalization we assume that all source domains follow certain domain index information that can reflect their domain relationships With this domain index information we propose a Multi Dimensional Domain Adaptation MDDA method to address this PLDG problem Specifically the MDDA method first trains multiple domain adaptation models to adapt from the labeled source domain to all the unlabeled source domains via adversarial learning Then those domain adaptation models and the source only model trained on the labeled source domain only are distilled into the target model used for the unseen target domain Theoretically we provide a generalization bound of the MDDA method The experiments on four real world datasets demonstrate the effectiveness of the proposed MDDA method
Please use this identifier to cite or link to this item: