Summary of Crowdtransfer: Enabling Crowd Knowledge Transfer in Aiot Community, by Yan Liu and Bin Guo and Nuo Li and Yasan Ding and Zhouyangzi Zhang and Zhiwen Yu
CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Community
by Yan Liu, Bin Guo, Nuo Li, Yasan Ding, Zhouyangzi Zhang, Zhiwen Yu
First submitted to arxiv on: 9 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 The paper presents a survey on Crowd Knowledge Transfer (CrowdTransfer), a novel concept that aims to reduce training costs and improve model performance in AIoT applications by leveraging prior knowledge learned from a crowd of agents. The authors introduce four transfer modes: derivation, sharing, evolution, and fusion, building upon conventional transfer learning methods. They also explore advanced crowd knowledge transfer models from three perspectives for various AIoT applications such as human activity recognition, urban computing, multi-robot system, and smart factory. Finally, they discuss open issues and outline future research directions in the AIoT community. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using artificial intelligence to help things (like devices or machines) work better together. It’s hard to make these things learn from each other without needing a lot of data or training time. The researchers are trying to find ways to share knowledge between different devices, so they can work together more efficiently. They look at four different methods for sharing this information and show how it can be used in real-world situations like recognizing human activities or controlling robots. |
Keywords
* Artificial intelligence * Activity recognition * Transfer learning
