Before revision:  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:  With this approach, the overfitting that the student network to the general features of the teacher network can be easily avoided . Persuasive experiments conducted on Cityscapes semantic segmentation with the prior knowledge transferred from 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)