Substep active deep learning framework for image classification

Abstract

In image classification, the acquisition of images labels is often expensive and time-consuming. To reduce this labeling cost, active learning is introduced into this field. Although some active learning algorithms have been proposed, they are all single-sampling strategies or combined with multiple-sampling strategies simultaneously (i.e., correlation, uncertainty and label-based measure), without considering the relationship between substep sampling strategies. To this end, we designed a new active learning scheme called substep active deep learning (SADL) for image classification. In SADL, samples were selected by correlation strategy and then determined by the uncertainty and label-based measurement. Finally, it is fed to CNN model training. Experiments were performed with three data sets (i.e., MNIST, Fashion-MNIST and CIFAR-10) to compare against state-of-the-art active learning algorithms, and it can be verified that our substep active deep learning is rational and effective.