Can adversarial training learn image captioning ?
This addresses the challenge of applying GANs to natural language generation for image captioning, which is an incremental step in a domain-specific area.
The paper tackles the problem of generating meaningful sentences for image captioning using adversarial training, proposing a conditional GAN architecture that achieves this without pre-training or reinforcement methods.
Recently, generative adversarial networks (GAN) have gathered a lot of interest. Their efficiency in generating unseen samples of high quality, especially images, has improved over the years. In the field of Natural Language Generation (NLG), the use of the adversarial setting to generate meaningful sentences has shown to be difficult for two reasons: the lack of existing architectures to produce realistic sentences and the lack of evaluation tools. In this paper, we propose an adversarial architecture related to the conditional GAN (cGAN) that generates sentences according to a given image (also called image captioning). This attempt is the first that uses no pre-training or reinforcement methods. We also explain why our experiment settings can be safely evaluated and interpreted for further works.