Cross-Domain Learning from Multiple Sources: A Consensus Regularization Perspective

By Fuzhen Zhuang, Ping Luo, Hui Xiong, Yuhong Xiong, Qing He, Zhongzhi Shi. 2010

69 citations recorded by the source corpus.

Subfields: Artificial Intelligence

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Keyu Duan
2. Dongbo Xi
3. Ignacio Fernández-Tobías
4. Qing He
5. Hui Xiong
6. Ning Xu
7. Qiufeng Wang
8. Shuxia Lin

Citations in the corpus

• Learngene: From Open-World to Your Learning Task (2021, background): “Transfer learning: Methods such as [26, 59, 60, 53, 36, 71, 15, 74, 16], which focus on transferring the knowledge across domains, aim at improving the performance of target learners in target domains.”
• A Comprehensive Survey on Transfer Learning (2019, background): “proposes a framework termed Consensus Regularization Framework (CRF) [115], [116].”
• Cross-Domain Recommender Systems (2015, background): “Rating distribution similarities Zhuang et al. 2010 [70] N (N) no overlap, (U) user overlap, (I) item overlap, (UI) user and item overlap.”