is well motivated and executed " [R4], that "tackles an important and challenging problem of few-shot fine-grained

We thank the reviewers for their constructive feedback. We are pleased that they appreciated our "novel paper that "The experiments, as well as the pilot study, are in great shape" [R4]. Our "framework works well on reasonably Generally speaking, a conditional GAN uses input noise conditioned on the label of the image to generate. BigGAN also follows this approach, but our fine-tuning technique uses a single image to train. We will clarify this point.