SelfReplay: Adapting Self-Supervised Sensory Models via Adaptive Meta-Task Replay

Self-supervised learning enables effective model pre-training on large-scale unlabeled data, which is crucial for user-specific fine-tuning in mobile sensing applications. However, pre-trained models often face significant domain shifts during fine-tuning due to user diversity, leading to performance degradation. To address this, we propose SelfReplay, an adaptive approach designed to align self-supervised models to different domains. SelfReplay consists of two stages: MetaSSL, which leverages meta-learning with self-supervised learning to pre-train domain-adaptive weights, and ReplaySSL, which further adapts the pre-trained model to each user's domain by replaying the meta-learned self-supervised task with a few user-specific samples. This produces a personalized model tailored to each user. Evaluations on mobile sensing benchmarks demonstrate that SelfReplay outperforms existing baselines, improving the F1-score by 9.4%p on average. On-device analyses on a commodity smartphone show the efficiency of SelfReplay's adaptation step, required just once after deployment, with SimCLR completing in only 10 seconds while using less than 100MB of memory.