You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: Conditional GANs. GAN-based conditional image generation has also been actively studied. Prior studies have provided both the discriminator and generator with class information in order to generate samples conditioned on the class  [20, 21, 22]. Other recent approaches focused on generating particular images highly relevant to a given text description  [25, 30]. The idea of conditional image generation has also been successfully applied to domain transfer [9, 28], super-resolution imaging[14], and photo editing [2, 27].In this paper, we propose a scalable GAN framework that can flexibly steer the image translation to various target domains, by providing conditional domain information.

Question: What point in StarGAN is valuable compared to Conditional GAN?
paper's model can process multiple different domains. According to the parer, several different paragraph compares previous researches and their model. However, there is no evidence that, that models are related to conditional GAN.