Summary of Ccs: Continuous Learning For Customized Incremental Wireless Sensing Services, by Qunhang Fu et al.
CCS: Continuous Learning for Customized Incremental Wireless Sensing Services
by Qunhang Fu, Fei Wang, Mengdie Zhu, Han Ding, Jinsong Han, Tony Xiao Han
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Wireless sensing has made significant progress in tasks such as action recognition, vital sign estimation, and pose estimation. As the technology reaches a tipping point, transitioning from proof-of-concept systems to large-scale deployment, we propose Continuous Customized Service (CCS) for wireless sensing model updates on users’ local computing resources without data transmission to service providers. To address catastrophic forgetting in model updates, we design knowledge distillation and weight alignment modules to enable the sensing model to acquire new capabilities while retaining existing ones. We conducted extensive experiments on the XRF55 dataset across Wi-Fi, millimeter-wave radar, and RFID modalities, demonstrating CCS’s excellence in continuous model services. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Wireless sensors can track people’s movements and vital signs from anywhere. But what if you want to add new features while keeping the old ones? This paper proposes a way to update sensing models on users’ devices without sending data to servers. We tested this approach on a large dataset, using Wi-Fi, radar, and RFID signals. Our results show that this method is better than others at handling changing demands. |
Keywords
» Artificial intelligence » Alignment » Knowledge distillation » Pose estimation
