# ARC-Fi: Exploiting Antenna Spatial Diversity for Label-Efficient Domain Generalization in Wi-Fi Sensing

arXiv: https://arxiv.org/abs/2310.06328 · HTML: https://mlsanity.com/paper/2310.06328/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 42 references that are in the map.
- First posted on arXiv 10 Oct 2023.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Learning Transferable Features with Deep Adaptation Networks](https://mlsanity.com/paper/1502.02791/) (2015)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [In Search of Lost Domain Generalization](https://mlsanity.com/paper/2007.01434/) (2020)
- [TS2Vec: Towards Universal Representation of Time Series](https://mlsanity.com/paper/2106.10466/) (2021)
- [Return of Frustratingly Easy Domain Adaptation](https://mlsanity.com/paper/1511.05547/) (2015)
- [Time-Series Representation Learning via Temporal and Contextual Contrasting](https://mlsanity.com/paper/2106.14112/) (2021)
- [Semi-Supervised Domain Generalization with Stochastic StyleMatch](https://mlsanity.com/paper/2106.00592/) (2021)
- [Federated Self-Supervised Learning of Multi-Sensor Representations for Embedded Intelligence](https://mlsanity.com/paper/2007.13018/) (2020)
- [Contrastive Domain Adaptation for Time-Series via Temporal Mixup](https://mlsanity.com/paper/2212.01555/) (2022)
- [WiGest: A Ubiquitous WiFi-based Gesture Recognition System](https://mlsanity.com/paper/1501.04301/) (2015)
- [AirFi: Empowering WiFi-based Passive Human Gesture Recognition to Unseen Environment via Domain Generalization](https://mlsanity.com/paper/2209.10285/) (2022)
- [Unsupervised Learning for Human Sensing Using Radio Signals](https://mlsanity.com/paper/2207.02370/) (2022)
- [Practical Issues and Challenges in CSI-based Integrated Sensing and Communication](https://mlsanity.com/paper/2204.03535/) (2022)

## Abstract (arXiv)

Wi-Fi sensing systems are severely hindered by domain shifts when deployed in unseen real-world environments. While existing methods attempt to tackle this through Unsupervised Domain Adaptation (UDA) or Domain Generalization (DG), they critically rely on either inaccessible target data or prohibitively expensive, massive labeled source datasets. In practice, collecting abundant unlabeled Channel State Information (CSI) is feasible, whereas manual labeling is severely constrained. This realistic dilemma necessitates Semi-Supervised Domain Generalization (SSDG). To this end, we propose ARC-Fi, the first dedicated SSDG framework for Wi-Fi sensing. Directly applying conventional contrastive learning to CSI data inevitably triggers paradigm-specific "shortcut learning," causing models to memorize environmental backgrounds rather than gesture dynamics. To overcome this, ARC-Fi introduces a physics-informed data augmentation strategy: the Antenna Response Consistency (ARC) module. ARC exploits the intrinsic spatial diversity of multi-antenna systems, treating signals from co-located antennas as naturally semantics-preserving augmented views to explicitly block environmental shortcuts. Furthermore, we introduce a unified Semi-Supervised Contrastive Objective that leverages scarce labels and reliable pseudo-labels to align cross-domain features, effectively preventing the blind repulsion of same-class instances. Extensive experiments on the Widar and CSIDA datasets demonstrate that ARC-Fi establishes a new state-of-the-art, significantly outperforming existing UDA, DG, and SSDG methods. Ultimately, this work provides a physics-grounded, label-efficient solution, advancing the scalable deployment of robust real-world Wi-Fi sensing systems. Code is available at: https://github.com/KaoruMiyazono/UniCrossFi.