Summary of Multi-surrogate-teacher Assistance For Representation Alignment in Fingerprint-based Indoor Localization, by Son Minh Nguyen et al.
Multi-Surrogate-Teacher Assistance for Representation Alignment in Fingerprint-based Indoor Localization
by Son Minh Nguyen, Linh Duy Tran, Duc Viet Le, Paul J.M Havinga
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 proposed plug-and-play (PnP) framework enables knowledge transfer for specialized networks in indoor localization by facilitating the exploitation of transferable representations directly on target Received Signal Strength (RSS) fingerprint datasets. The framework consists of two phases: Expert Training, which utilizes multiple surrogate generative teachers to homogenize input disparities among source RSS datasets while preserving unique characteristics; and Expert Distilling, which refines the representation learning of specialized networks on the target dataset through minimizing differences in essential knowledge between the network and surrogate teachers. This approach fosters representational alignment that is less sensitive to specific environmental dynamics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The PnP framework helps specialize networks for indoor localization by transferring knowledge from one RSS fingerprint dataset to another. It does this by using multiple teacher models to adapt to different environments, then fine-tunes the student network to match the target environment. This approach improves the performance of specialized networks in localizing devices indoors. |
Keywords
» Artificial intelligence » Alignment » Representation learning
