Before revision:  The Image Party distillation enhancement method(IP) is also proposed, which shuffling images from various domains, and randomly selecting a few as mini-batch. With this approach, the overfitting that the student network to the general features of the teacher network can be easily avoided . Persuasive experiments and discussions are conducted on CityScapes with the prompt of COCO2017 and KITTI . Results demonstrate the boosting performance in segmentation (mIOU and high-precision accuracy boost by 1.4\% and 8.2\% respectively, with 78.2\% output variance)
After revision:  The Image Party distillation enhancement method(IP) is also proposed, which commits to exploit a more comprehensive general features extraction capability by considering images from both the target and the proximity domains as input. To our knowledge, this paper is a pioneering work on the application of knowledge distillation to few-shot learning . Persuasive experiments and discussions are conducted on CityScapes with the prompt of COCO2017 and KITTI . Results demonstrate the boosting performance in segmentation (mIOU and high-precision accuracy boost by 1.4\% and 8.2\% respectively, with 78.2\% output variance)