Towards Better Uncertainty Sampling: Active Learning with Multiple Views for Deep Convolutional Neural Network

Abstract

Convolutional neural network (CNN) has been successfully applied to many fields, such as image classification and object detection. It relies on huge amount of data. However, labelling a large amount of data is expensive. Active learning is one of the approaches to alleviate the labelling effort. We propose a new active learning approach for CNN. Different from existing active learning algorithms for CNN, first, the active query strategy is measured from multiple views, not only the last output of CNN; second, multiple views are obtained from multiple hidden layers in CNN, not from other related data or models. We evaluate our approach on three widely used datasets: Fashion-MNIST, SVHN and CIFAR-10. Experimental results show that the proposed method outperforms baseline methods in image classification.