Recent successes in learning-based image classification, however, heavily
rely on the large number of annotated training samples, which may require
considerable human efforts. In this paper, we propose a novel active learning
framework, which is capable of building a competitive classifier with optimal
feature representation via a limited amount of labeled training instances in an
incremental learning manner. Our approach advances the existing active learning
methods in two aspects. First, we incorporate deep convolutional neural
networks into active learning. Through the properly designed framework, the
feature representation and the classifier can be simultaneously updated with
progressively annotated informative samples. Second, we present a
cost-effective sample selection strategy to improve the classification
performance with less manual annotations. Unlike traditional methods focusing
on only the uncertain samples of low prediction confidence, we especially
discover the large amount of high confidence samples from the unlabeled set for
feature learning. Specifically, these high confidence samples are automatically
selected and iteratively assigned pseudo-labels. We thus call our framework
"Cost-Effective Active Learning" (CEAL) standing for the two
advantages.Extensive experiments demonstrate that the proposed CEAL framework
can achieve promising results on two challenging image classification datasets,
i.e., face recognition on CACD database [1] and object categorization on
Caltech-256 [2].

本文提出了一种新的主动学习框架，通过有限量的标签训练实例以增量学习的方式构建具有最佳特征表示的竞争性分类器。该方法利用深度卷积神经网络进行主动学习，设计了一种成本效益的样本选择策略，通过选取高置信度的无标签样本自动迭代分配伪标签来改善分类性能。实验证明，该框架在人脸识别和物体分类等两个数据集上取得了有希望的成果。