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.
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This project was supported by the National Natural Science Foundation of China (Grant No. 61403331), Program for the Top Young Talents of Higher Learning Institutions of He Bei (Grant No. BJ2017033), Natural Science Foundation of He Bei Province (Grant No. F2016203427) and China Postdoctoral Science Foundation (Grant No. 2015M571280).
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Li, G., Gong, N. Substep active deep learning framework for image classification. Pattern Anal Applic (2020). https://doi.org/10.1007/s10044-020-00894-5
- Convolutional neural network
- Active learning
- Image classification