  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.
