Sufficient supervised information is crucial for any machine learning models
to boost performance. However, labeling data is expensive and sometimes
difficult to obtain. Active learning is an approach to acquire annotations for
data from a human oracle by selecting informative samples with a high
probability to enhance performance. In recent emerging studies, a generative
adversarial network (GAN) has been integrated with active learning to generate
good candidates to be presented to the oracle. In this paper, we propose a
novel model that is able to obtain labels for data in a cheaper manner without
the need to query an oracle. In the model, a novel reward for each sample is
devised to measure the degree of uncertainty, which is obtained from a
classifier trained with existing labeled data. This reward is used to guide a
conditional GAN to generate informative samples with a higher probability for a
certain label. With extensive evaluations, we have confirmed the effectiveness
of the model, showing that the generated samples are capable of improving the
classification performance in popular image classification tasks.

本研究提出了一种结合有监督的条件生成对抗网络和主动学习的新模型，以更小的代价获得数据的标记，且生成的样本能有效地提高流行的图像分类任务中的分类性能。