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arXiv · 1910.12246

Prediction stability as a criterion in active learning

Junyu Liu·Xiang Li·Jin Wang·Jiqiang Zhou·Jianxiong Shen
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Abstract

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

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Junyu Liu, Xiang Li, Jin Wang, Jiqiang Zhou, Jianxiong Shen. 2019-10-27. Prediction stability as a criterion in active learning. https://arxiv.org/abs/1910.12246

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