Summary of Aglp: a Graph Learning Perspective For Semi-supervised Domain Adaptation, by Houcheng Su et al.
AGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation
by Houcheng Su, Mengzhu Wang, Jiao Li, Nan Yin, Liang Yang, Li Shen
First submitted to arxiv on: 20 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a new approach to semi-supervised domain adaptation (SSDA) by leveraging graph learning perspectives. The model, called AGLP, utilizes graph convolutional networks to incorporate structural information into the instance graph, allowing for better propagation of features along weighted edges. This innovation allows for more effective learning of domain-invariant and semantic representations, reducing domain discrepancies in SSDA. Compared to existing state-of-the-art methods, AGLP outperforms them on multiple standard benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores a new way to improve semi-supervised domain adaptation by using graph learning. It’s an innovative approach that helps machines learn from different types of data with minimal labeled information. The proposed model is called AGLP and uses special networks to connect pieces of information together, allowing it to better understand the relationships between them. This leads to better results for tasks like image recognition and language translation. |
Keywords
» Artificial intelligence » Domain adaptation » Semi supervised » Translation
