LAUSR

Abstract. We aim to improve the negative-accelerated sampling framework and construct a more reasonable and effective active sampling framework by introducing the technique of reinforcement learning. Compared with traditional uncertainty-based active sampling strategy, the proposed sample selection framework consists of both a certainty metric and sample postprocessing for more precise evaluation. The certainty metric is measured by the visual classifying model, and the postprocessing module is implemented by the Q-learning algorithm to construct a compact training set for the visual module to further improve the effectiveness and efficiency of classification. Meanwhile, the parameters of the whole sampling framework are calculated adaptively instead of being set manually to improve the adaptiveness of the whole framework. Experimental results on real-world datasets show the effectiveness of the proposed framework.