# Semi-Self Representation Learning for Crowdsourced WiFi Trajectories

arXiv: https://arxiv.org/abs/2504.03756 · HTML: https://mlsanity.com/paper/2504.03756/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 2 Apr 2025.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Using Synthetic Data to Enhance the Accuracy of Fingerprint-Based Localization: A Deep Learning Approach](https://mlsanity.com/paper/2105.01903/) (2021)
- [Towards Robust Crowdsourcing-Based Localization: A Fingerprinting Accuracy Indicator Enhanced Wireless/Magnetic/Inertial Integration Approach](https://mlsanity.com/paper/2004.04688/) (2020)
- [Enabling Plug-and-Play and Crowdsourcing SLAM in Wireless Communication Systems](https://mlsanity.com/paper/2108.03609/) (2021)

## Abstract (arXiv)

WiFi fingerprint-based localization has been studied intensively. Point-based solutions rely on position annotations of WiFi fingerprints. Trajectory-based solutions, however, require end-position annotations of WiFi trajectories, where a WiFi trajectory is a multivariate time series of signal features. A trajectory dataset is much larger than a pointwise dataset as the number of potential trajectories in a field may grow exponentially with respect to the size of the field. This work presents a semi-self representation learning solution, where a large dataset C of crowdsourced unlabeled WiFi trajectories can be automatically labeled by a much smaller dataset tilde C of labeled WiFi trajectories. The size of tilde C only needs to be proportional to the size of the physical field, while the unlabeled C could be much larger. This is made possible through a novel ``cut-and-flip'' augmentation scheme based on the meet-in-the-middle paradigm. A two-stage learning consisting of trajectory embedding followed by endpoint embedding is proposed for the unlabeled C. Then the learned representations are labeled by tilde C and connected to a neural-based localization network. The result, while delivering promising accuracy, significantly relieves the burden of human annotations for trajectory-based localization.