1. The guidance over SIFT feature space is good. However, the perceptual losses (such as VGG feature loss) are also considered effective. The authors should clarify their choice, otherwise this contribution is weakened. 2. Assumption 3.1. says the loss of TKD is assumed less than IYOR. However, eqn.7 tells a different story. 3. Assumption 3.1. may not hold in real cases. One cannot increase the parameter number of the teacher network when applying a KD algorithm.
4. This paper can become more solid if IYOR is used in some modern i2i methods. i.e, StyleFlow, EGSDE etc. 5. The student and refinement networks are trained simultaneously. Which may improve the performance of the teacher network. Is the comparison fair? Please provide KID/FID metrics of your teacher network.
5. The student and refinement networks are trained simultaneously. Which may improve the performance of the teacher network. Is the comparison fair? Please provide KID/FID metrics of your teacher network.