Dual generative adversarial active learning

The purpose of active learning is to significantly reduce the cost of annotation while ensuring the good performance of the model. In this paper, we propose a novel active learning method based on the combination of pool and synthesis named dual generative adversarial active learning (DGAAL), which includes the functions of image generation and representation learning. This method includes two groups of generative adversarial network composed of a generator and two discriminators. One group is used for representation learning, and then this paper performs sampling based on the predicted value of the discriminator. The other group is used for image generation. The purpose is to generate samples which are similar to those obtained from sampling, so that samples with rich information can be fully utilized. In the sampling process, the two groups of network cooperate with each other to enable the generated samples to participate in sampling process, and to enable the discriminator for sampling to co-evolve. Thus, in the later stage of sampling, the problem of insufficient information for selecting samples based on the pool method is alleviated. In this paper, DGAAL is evaluated extensively on three data sets, and the results show that DGAAL not only has certain advantages over the existing methods in terms of model performance but can also further reduces the annotation cost.