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) , and can gain a precise compact network with only 41.8  %DIF < FLOPs(see Fig. 1).
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 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\% segmentation variance) with only 41.8  %DIF < FLOPs(see Fig. 1).