Making the torch lighter: Areinforced active sampling framework for image classification

In this paper, we aim to construct a more reasonable and effective active sampling model, named as reinforcement uncertainty sampling with bag-of-visual-words (RUSB). Compared with traditional active sampling strategy based on uncertainty, both certainty metric and sample post-processing are introduced for better performance. The certainty metric is measured by the bag-of-visual-words (BoVW) classification model in order to entirely evaluate samples, and the post-processing module is driven by the Q-learning method to construct a compact and efficient training set for the BoVW module. The performance of BoVW is used to initialize and determine the status of the post-processing module during the process of iteration. Meanwhile, the weight of the measurement is associated with each iteration instead of being set manually. Experimental results on real world datasets show the effectiveness of the proposed framework.