ContrastSense: domain-invariant contrastive learning for in-the-wild wearable sensing

• Main Authors: Dai, Gaole, Xu, Huatao, Yoon, Hyungjun, Li, Mo, Tan, Rui, Lee, Sung-Ju
• 其他作者: College of Computing and Data Science
• 格式: Article
• 語言: English
• 出版: 2025
• 主題: Computer and Information Science; Wearable Sensing; Contrastive Learning
• 在線閱讀: https://hdl.handle.net/10356/182272
• 機構: Nanyang Technological University

Existing wearable sensing models often struggle with domain shifts and class label scarcity. Contrastive learning is a promising technique to address class label scarcity, which however captures domain-related features and suffers from low-quality negatives. To address both problems, we propose Cont...