The success of deep learning applications relies on a large number of labeled data. Active learning aims at identifying most informative unlabeled samples for labeling so as to achieve comparable performance with as few labeled data as possible. A two-stage batch-mode active learning (TBAL) method is proposed in this work. In the first stage of TBAL, we propose a new semi-supervised learning framework that offers accurate uncertainty estimation and finds a high-dimensional feature representation for unlabeled samples. In other words, it leverages a small number of labeled data as well as a large number of unlabeled data. In the second stage of TBAL, we propose a novel technique called cluster re-balancing (CRB). It takes correlations within a batch into account and fits a query strategy in batch mode. The TBAL method can obtain informative batches of samples by considering uncertainty and diversity jointly in a high-dimensional feature space. Moreover, in order to compare the robustness of semi-supervised learning based active learning methods, a new evaluation protocol based on transfer learning is introduced. Experiments on SVHN, CIFAR-10 and CUB-200-2011 datasets show that our proposed CRB based query strategy that employs a semi-supervised model with a good balance between uncertainty and diversity outperforms various baselines as well as other state-of-the-art methods by a clear margin.

深度学习应用的成功依赖于大量的标记数据。主动学习旨在识别信息量最大的未标记样本进行标记，从而以尽可能少的标记数据实现可比性能。在这项工作中提出了一种两阶段批处理模式主动学习（TBAL）方法。在 TBAL 的第一阶段，我们提出了一种新的半监督学习框架，该框架提供准确的不确定性估计，并为未标记的样本找到高维特征表示。换句话说，它利用了少量标记数据以及大量未标记数据。在 TBAL 的第二阶段，我们提出了一种称为集群重新平衡 (CRB) 的新技术。它考虑了批处理中的相关性，并适合批处理模式下的查询策略。TBAL 方法可以通过在高维特征空间中联合考虑不确定性和多样性来获得信息量大的样本批次。此外，为了比较基于半监督学习的主动学习方法的鲁棒性，引入了一种基于迁移学习的新评估协议。在 SVHN、CIFAR-10 和 CUB-200-2011 数据集上的实验表明，我们提出的基于 CRB 的查询策略采用半监督模型，在不确定性和多样性之间取得了良好的平衡，优于各种基线以及其他状态。艺术方法的优势明显。介绍了一种基于迁移学习的新评估协议。在 SVHN、CIFAR-10 和 CUB-200-2011 数据集上的实验表明，我们提出的基于 CRB 的查询策略采用半监督模型，在不确定性和多样性之间取得了良好的平衡，优于各种基线以及其他状态。艺术方法的优势明显。介绍了一种基于迁移学习的新评估协议。在 SVHN、CIFAR-10 和 CUB-200-2011 数据集上的实验表明，我们提出的基于 CRB 的查询策略采用半监督模型，在不确定性和多样性之间取得了良好的平衡，优于各种基线以及其他状态。艺术方法的优势明显。