What Makes Good Contrastive Learning on Small-Scale Wearable-based Tasks?
Open accessAbstract
Self-supervised learning establishes a new paradigm of learning representations with much fewer or even no label annotations. Recently there has been remarkable progress on large-scale contrastive learning models which require substantial computing resources, yet such models are not practically optimal for small-scale tasks. To fill the gap, we aim to study contrastive learning on the wearable-based activity recognition task. Specifically, we conduct an in-depth study of contrastive learning from both algorithmic-level and task-level perspectives. For algorithmic-level analysis, we decompose contrastive models into several key components and conduct rigorous experimental evaluations to better understand the efficacy and rationale behind contrastive learning. More importantly, for task-level analysis, we show that the wearable-based signals bring unique challenges and opportunities to existing contrastive models, which cannot be readily solved by existing algorithms. Our thorough empirical studies suggest important practices and shed light on future research challenges. In the meantime, this paper presents an open-source PyTorch library CL-HAR, which can serve as a practical tool for researchers. The library is highly modularized and easy to use, which opens up avenues for exploring novel contrastive models quickly in the future.
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Consistent with prior studies, excessively large batch sizes can reduce gradient diversity and introduce optimization instability.
“Consistent with prior studies [46], excessively large batch sizes can reduce gradient diversity and introduce optimization instability.”
From Towards User-Generalizable Wearable-Sensor-Based Human Activity Recognition: A Multi-Task Contrastive Learning Approach (2025) · cites this paper as [46]
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- Authors
- Qian, H., Tian, T., & Miao, C.
- Affiliations
- Nanyang Technological University (SG)
- Venue
- Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
- Publisher
- ACM
- Published
- 2022-08-14
- Pages
- 3761-3771
- OpenAlex
- W4291125982
- Cited by
- 67
- References
- 17
- Citations by year
- 2026: 7 · 2025: 28 · 2024: 17 · 2023: 13 · 2022: 2
- FWCI
- 8.29
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Keywords
- contrastive learning, self-supervised learning, wearable sensors, activity recognition, representation learning
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