ADAPT^2: Adapting Pre-Trained Sensing Models to End-Users via Self-Supervision Replay
<|reference_start|>ADAPT^2: Adapting Pre-Trained Sensing Models to End-Users via Self-Supervision Replay: Self-supervised learning has emerged as a method for utilizing massive unlabeled data for pre-training models, providing an effective feature extractor for various mobile sensing applications. However, when deployed to end-users, these models encounter significant domain shifts attributed to user diversity. We investigate the performance degradation that occurs when self-supervised models are fine-tuned in heterogeneous domains. To address the issue, we propose ADAPT^2, a few-shot domain adaptation framework for personalizing self-supervised models. ADAPT2 proposes self-supervised meta-learning for initial model pre-training, followed by a user-side model adaptation by replaying the self-supervision with user-specific data. This allows models to adjust their pre-trained representations to the user with only a few samples. Evaluation with four benchmarks demonstrates that ADAPT^2 outperforms existing baselines by an average F1-score of 8.8%p. Our on-device computational overhead analysis on a commodity off-the-shelf (COTS) smartphone shows that ADAPT2 completes adaptation within an unobtrusive latency (in three minutes) with only a 9.54% memory consumption, demonstrating the computational efficiency of the proposed method.<|reference_end|>
@article{yoon2024adapt^2:,
    title={ADAPT^2: Adapting Pre-Trained Sensing Models to End-Users via
  Self-Supervision Replay},
    author={Hyungjun Yoon, Jaehyun Kwak, Biniyam Aschalew Tolera, Gaole Dai, Mo
  Li, Taesik Gong, Kimin Lee and Sung-Ju Lee},
    journal={arXiv preprint arXiv:2404.15305},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.15305},
    primaryClass={eess.SP cs.LG}
}