Towards Data-Efficient Continuous Learning for Edge Video Analytics via Smart Caching

Continuous learning (CL) has recently been adopted into edge video analytics, gaining huge success in maintaining high accuracy without constantly retraining DNN models by human intervention. Though existing solutions offer optimized processing pipelines, the cost brought by CL should not be neglected. This vision paper starts an investigation by exploring two kinds of cost, human labeling and edge storage. The former comes from the need for CL's automatically tuning, and the latter is due to an exemplar pool (including both drift and historical data) maintained to prevent catastrophic forgetting caused by naive retraining. To alleviate the costs, we propose a new CL-based edge video analytics system by incorporating an active learner mechanism. Specifically, we revisit the current CL video system design and develop an active CL pipeline atop them. The pipeline first accepts the drift data stored in drift pool and utilizes an active learner to sample a small partition of them for labeling. Then it mixes up both small labeled drifted data and some historical data to send them to an exemplar pool for CL. Our preliminary benchmark studies exhibit that the new system can achieve competitive accuracy by spending only 30% labeling and storage cost compared to other baselines, showing a promising research direction for future study.