Prediction stability as a criterion in active learning

Conference Article


International Conference on Artificial Neural Networks (ICANN)





Doc link


Download the digital copy of the doc pdf document



Recent breakthroughs made by deep learning rely heavily on a large number of annotated samples. To overcome this shortcoming, active learning is a possible solution. Besides the previous active learning algorithms that only adopted information after training, we propose a new class of methods named sequential-based method based on the information during training. A specific criterion of active learning called prediction stability is proposed to prove the feasibility of sequential-based methods. We design a toy model to explain the principle of our proposed method and pointed out a possible defect of the former uncertainty-based methods. Experiments are made on CIFAR-10 and CIFAR-100, and the results indicates that prediction stability was effective and works well on fewer-labeled datasets. Prediction stability reaches the accuracy of traditional acquisition functions like entropy on CIFAR-10, and notably outperformed them on CIFAR-100.


pattern recognition.

Author keywords

deep learning, active learning, classification, sequential-based, prediction stability

Scientific reference

J. Liu, X. Li, J. Zhou and J. Shen. Prediction stability as a criterion in active learning, 2020 International Conference on Artificial Neural Networks, 2020, Bratislava, Slovakia, in Artificial Neural Networks and Machine Learning – ICANN 2020, Vol 12397 of Lecture Notes in Computer Science, pp. 157-167, 2020, Springer, Cham, Switzerland.