# KNN-MMD: Cross Domain Wireless Sensing via Local Distribution Alignment

arXiv: https://arxiv.org/abs/2412.04783 · HTML: https://mlsanity.com/paper/2412.04783/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 53 references that are in the map.
- First posted on arXiv 6 Dec 2024.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [Domain Adaptation for Visual Applications: A Comprehensive Survey](https://mlsanity.com/paper/1702.05374/) (2017)
- [Deep Subdomain Adaptation Network for Image Classification](https://mlsanity.com/paper/2106.09388/) (2021)
- [Towards an Intelligent Edge: Wireless Communication Meets Machine Learning](https://mlsanity.com/paper/1809.00343/) (2018)
- [Grassmannian Learning: Embedding Geometry Awareness in Shallow and Deep Learning](https://mlsanity.com/paper/1808.02229/) (2018)
- [An overview of domain-specific foundation model: key technologies, applications and challenges](https://mlsanity.com/paper/2409.04267/) (2024)
- [AutoFi: Towards Automatic WiFi Human Sensing via Geometric Self-Supervised Learning](https://mlsanity.com/paper/2205.01629/) (2022)
- [AirFi: Empowering WiFi-based Passive Human Gesture Recognition to Unseen Environment via Domain Generalization](https://mlsanity.com/paper/2209.10285/) (2022)
- [Finding the Missing Data: A BERT-inspired Approach Against Package Loss in Wireless Sensing](https://mlsanity.com/paper/2403.12400/) (2024)
- [Deep Learning for Radio-based Human Sensing: Recent Advances and Future Directions](https://mlsanity.com/paper/2010.12717/) (2020)
- [CrossFi: A Cross Domain Wi-Fi Sensing Framework Based on Siamese Network](https://mlsanity.com/paper/2408.10919/) (2024)
- [CRISLoc: Reconstructable CSI Fingerprintingfor Indoor Smartphone Localization](https://mlsanity.com/paper/1910.06895/) (2019)
- [Wi-Fi 8: Embracing the Millimeter-Wave Era](https://mlsanity.com/paper/2309.16813/) (2023)

## Built on by (most cited first, 3 of 3)

- [Towards Trustworthy Wi-Fi CSI-based Sensing: Systematic Evaluation of Adversarial Robustness](https://mlsanity.com/paper/2511.20456/) (2025)
- [FuseFi: Combining Irregularly Sampled CSI from Diverse Communication Packets and Frequency Bands for Wi-Fi Sensing](https://mlsanity.com/paper/2512.22143/) (2025)
- [Parameter-Efficient Domain Adaption for CSI Crowd-Counting via Self-Supervised Learning with Adapter Modules](https://mlsanity.com/paper/2601.02203/) (2026)

## Abstract (arXiv)

Wireless sensing has recently found widespread applications in diverse environments, including homes, offices, and public spaces. By analyzing patterns in channel state information (CSI), it is possible to infer human actions for tasks such as person identification, gesture recognition, and fall detection. However, CSI is highly sensitive to environmental changes, where even minor alterations can significantly distort the CSI patterns. This sensitivity often leads to performance degradation or outright failure when applying wireless sensing models trained in one environment to another. To address this challenge, Domain Alignment (DAL) has been widely adopted for cross-domain classification tasks, as it focuses on aligning the global distributions of the source and target domains in feature space. Despite its popularity, DAL often neglects inter-category relationships, which can lead to misalignment between categories across domains, even when global alignment is achieved. To overcome these limitations, we propose K-Nearest Neighbors Maximum Mean Discrepancy (KNN-MMD), a novel few-shot method for cross-domain wireless sensing. Our approach begins by constructing a help set using KNN from the target domain, enabling local alignment between the source and target domains within each category using MMD. Additionally, we address a key instability issue commonly observed in cross-domain methods, where model performance fluctuates sharply between epochs. Further, most existing methods struggle to determine an optimal stopping point during training due to the absence of labeled data from the target domain. Our method resolves this by excluding the support set from the target domain during training and employing it as a validation set to determine the stopping criterion. The dataset and code are publicly available at https://github.com/RS2002/KNN-MMD .