  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.
