Collaboration by Competition: Self-coordinated Knowledge Amalgamation for Multi-talent Student Learning

By Sihui Luo, Wenwen Pan, Xinchao Wang, Dazhou Wang, Haihong Tang, Mingli Song. 2020

22 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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. Dacheng Tao
2. Xinchao Wang
3. Jianping Gou
4. S. Maybank
5. Baosheng Yu
6. Mingli Song
7. Yiding Yang
8. Yongcheng Jing

In Computer Vision and Pattern Recognition

1. Dacheng Tao
2. Xinchao Wang
3. Mingli Song
4. Jianping Gou
5. S. Maybank
6. Baosheng Yu
7. Yiding Yang
8. Yongcheng Jing

Citations in the corpus

• Knowledge Distillation: A Survey (2020, methodology): “Using multiple teacher models with feature ensembles, knowledge amalgamation is designed in (Shen et al. 2019a; Luo et al. 2019; Shen et al. 2019b; Luo et al. 2020).”
• Amalgamating Knowledge from Heterogeneous Graph Neural Networks (2021, background): “Also, the works in [32, 34, 22, 45] propose to reuse multiple trained teacher CNNs, working on different tasks, to learn a versatile student model, but built upon a strong assumption that the teacher models share the same CNN architecture.”