# Chorus: Harmonizing Context and Sensing Signals for Data-Free Model Customization in IoT

arXiv: https://arxiv.org/abs/2512.15206 · HTML: https://mlsanity.com/paper/2512.15206/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 64 references that are in the map.
- First posted on arXiv 17 Dec 2025.

## Builds on (most cited first, 25 of 25)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [In Search of Lost Domain Generalization](https://mlsanity.com/paper/2007.01434/) (2020)
- [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://mlsanity.com/paper/2103.03097/) (2021)
- [Deep Visual Domain Adaptation: A Survey](https://mlsanity.com/paper/1802.03601/) (2018)
- [A Transformer-based Framework for Multivariate Time Series Representation Learning](https://mlsanity.com/paper/2010.02803/) (2020)
- [A Survey of Unsupervised Deep Domain Adaptation](https://mlsanity.com/paper/1812.02849/) (2018)
- [A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts](https://mlsanity.com/paper/2303.15361/) (2023)
- [Multimodal Deep Learning](https://mlsanity.com/paper/2301.04856/) (2023)
- [Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models](https://mlsanity.com/paper/1911.03393/) (2019)
- [Joint Multimodal Learning with Deep Generative Models](https://mlsanity.com/paper/1611.01891/) (2016)
- [On Learning Invariant Representation for Domain Adaptation](https://mlsanity.com/paper/1901.09453/) (2019)
- [WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series](https://mlsanity.com/paper/2203.09978/) (2022)
- [EdgeFM: Leveraging Foundation Model for Open-set Learning on the Edge](https://mlsanity.com/paper/2311.10986/) (2023)
- [Optimization-Free Test-Time Adaptation for Cross-Person Activity Recognition](https://mlsanity.com/paper/2310.18562/) (2023)
- [Domain Generalization for Activity Recognition via Adaptive Feature Fusion](https://mlsanity.com/paper/2207.11221/) (2022)
- [Cross-modal Variational Auto-encoder for Content-based Micro-video Background Music Recommendation](https://mlsanity.com/paper/2107.07268/) (2021)
- [CSI-Bench: A Large-Scale In-the-Wild Dataset for Multi-task WiFi Sensing](https://mlsanity.com/paper/2505.21866/) (2025)
- [M3BAT: Unsupervised Domain Adaptation for Multimodal Mobile Sensing with Multi-Branch Adversarial Training](https://mlsanity.com/paper/2404.17391/) (2024)
- [Monitoring and Adapting ML Models on Mobile Devices](https://mlsanity.com/paper/2305.07772/) (2023)
- [DGSense: A Domain Generalization Framework for Wireless Sensing](https://mlsanity.com/paper/2502.08155/) (2025)
- [A Systematic Survey on Android API Usage for Data-Driven Analytics with Smartphones](https://mlsanity.com/paper/2104.11271/) (2021)
- [COMPASS: Unsupervised and Online Clustering of Complex Human Activities from Smartphone Sensors](https://mlsanity.com/paper/2306.15437/) (2023)

## Built on by (most cited first, 1 of 1)

- [Towards Trustworthy 6G Network Digital Twins: A Framework for Validating Counterfactual What-If Analysis in Edge Computing Resources](https://mlsanity.com/paper/2604.14787/) (2026)

## Abstract (arXiv)

A key bottleneck toward scalable IoT sensing is efficiently adapting trained AI models to new deployment conditions. Context shifts, such as changes in sensor placement or ambient environments, can substantially alter sensing patterns and degrade model performance. We present Chorus, a context-bridged, data-free post-deployment model customization approach that adapts sensing models to unseen contexts without requiring target-domain sensor data or post-deployment retraining. Chorus learns compact, transferable context representations and aligns them with the sensor latent space using unlabeled sensor-context pairs, bridging context generalization with sensing-data generalization. It then uses a lightweight gated prediction head to integrate context priors at inference and an adaptive caching mechanism to reuse context representations when no context shift is detected, reducing on-device overhead. Experiments on IMU sensing, speech enhancement, and WiFi sensing under diverse context shifts show that Chorus outperforms state-of-the-art baselines by up to 20.2% in unseen contexts, achieves inference latency comparable to sensor-only deployment, and remains stable under continuous context transitions and varied context descriptions. A video demonstration is available at https://youtu.be/yANTZsk0TVU.