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Erschienen in:
2020 | OriginalPaper | Buchkapitel
Prediction Stability as a Criterion in Active Learning
verfasst von : Junyu Liu, Xiang Li, Jiqiang Zhou, Jianxiong Shen
Erschienen in: Artificial Neural Networks and Machine Learning – ICANN 2020
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Abstract
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.