Comprehensive and Efficient Data Labeling via Adaptive Model Scheduling

Mu Yuan, Lan Zhang, Xiang-Yang Li, Hui Xiong

Abstract: Labeling data comprehensively and efficiently is a widely needed but challenging task. With limited computing resources, given a data stream and a collection of deep-learning models, we propose to adaptively select and schedule a subset of these models to execute, aiming to maximize the value of the model output. Achieving this goal is nontrivial since a model's output on any data item is content-dependent and hard to predict. In this paper, we present an Adaptive Model Scheduling framework, consisting of 1) a deep reinforcement learning-based approach to predict the value of unexecuted models by mining semantic relationship among diverse models, and 2) two heuristic algorithms to adaptively schedule models under deadline or deadline-memory constraints. The proposed framework does not require any prior knowledge of the data, which works as a powerful complement to existing model optimization technologies. We conduct extensive evaluations on 30 popular image labeling models to demonstrate the effectiveness of our design.