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: Another important advantage of our model is the scalability in terms of the number of parameters required. The last column in Table 3 shows that the number of parameters required to learn all translations by StarGAN is seven times smaller than that of DIAT and fourteen times smaller than that of CycleGAN. This is because StarGAN requires only a single generator and discriminator pair, regardless of the number of domains, while in the case of cross-domain models such as CycleGAN, a completely different model should be trained for each source-target domain pair.

Question: They propose StarGAN to overcome a limitation of high computational complexity of current image-to-image translation models. Is it true?
It is false. There is no evidence that the motivation of StarGAN was made in order to overcome a limitation of high computational complexity, even it achieved that.