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[2510.02276] BioX-Bridge: Model Bridging for Unsupervised Cross-Modal Knowledge Transfer across Biosignals
Summary
The paper introduces BioX-Bridge, a framework for unsupervised cross-modal knowledge transfer in biosignals, enhancing model efficiency while maintaining performance across modalities.
Why It Matters
This research addresses the challenge of limited labeled datasets in biosignal analysis by proposing a novel approach to leverage existing knowledge for training models in new modalities. This has significant implications for health monitoring systems, making them more accessible and adaptable.
Key Takeaways
- BioX-Bridge significantly reduces trainable parameters by 88-99%.
- The framework enables efficient knowledge transfer across different biosignal modalities.
- It improves model performance without the high computational overhead typically associated with existing methods.
Computer Science > Artificial Intelligence arXiv:2510.02276 (cs) [Submitted on 2 Oct 2025 (v1), last revised 24 Feb 2026 (this version, v2)] Title:BioX-Bridge: Model Bridging for Unsupervised Cross-Modal Knowledge Transfer across Biosignals Authors:Chenqi Li, Yu Liu, Timothy Denison, Tingting Zhu View a PDF of the paper titled BioX-Bridge: Model Bridging for Unsupervised Cross-Modal Knowledge Transfer across Biosignals, by Chenqi Li and 3 other authors View PDF HTML (experimental) Abstract:Biosignals offer valuable insights into the physiological states of the human body. Although biosignal modalities differ in functionality, signal fidelity, sensor comfort, and cost, they are often intercorrelated, reflecting the holistic and interconnected nature of human physiology. This opens up the possibility of performing the same tasks using alternative biosignal modalities, thereby improving the accessibility, usability, and adaptability of health monitoring systems. However, the limited availability of large labeled datasets presents challenges for training models tailored to specific tasks and modalities of interest. Unsupervised cross-modal knowledge transfer offers a promising solution by leveraging knowledge from an existing modality to support model training for a new modality. Existing methods are typically based on knowledge distillation, which requires running a teacher model alongside student model training, resulting in high computational and memory overhead. This chall...
