GeAE: GAE-Embedded Autoencoder Based Causal Representation for Robust Domain Adaptation
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2024, 00, pp. 3777-3782
- Issue Date:
- 2024-01-29
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 1705147.pdf | Published version | 838.67 kB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
In this work we study the unsupervised robust domain adaptation problem where only a single well labeled source domain data is available during the learning process A new causal representation method based on a Graph autoen coder embedded AutoEncoder named GeAE is introduced to learn invariant representations across domains for robust domain adaption The proposed method can handle nonlinear causal relations included in the data by a causal structure learning process similar to a graph autoencoder Moreover the cross entropy loss as well as the causal structure loss and the reconstruction loss are incorporated in the objective function designed in a united autoencoder to improve the quality of predictions using causal representations Experimental results on one generated dataset and three real world datasets demonstrate the effectiveness of GeAE in comparison with the state of the art methods
Please use this identifier to cite or link to this item: