  Knowledge distillation has been applied to various tasks successfully. The
current distillation algorithm usually improves students' performance by
imitating the output of the teacher. This paper shows that teachers can also
improve students' representation power by guiding students' feature recovery.
From this point of view, we propose Masked Generative Distillation (MGD), which
is simple: we mask random pixels of the student's feature and force it to
generate the teacher's full feature through a simple block. MGD is a truly
general feature-based distillation method, which can be utilized on various
tasks, including image classification, object detection, semantic segmentation
and instance segmentation. We experiment on different models with extensive
datasets and the results show that all the students achieve excellent
improvements. Notably, we boost ResNet-18 from 69.90% to 71.69% ImageNet top-1
accuracy, RetinaNet with ResNet-50 backbone from 37.4 to 41.0 Boundingbox mAP,
SOLO based on ResNet-50 from 33.1 to 36.2 Mask mAP and DeepLabV3 based on
ResNet-18 from 73.20 to 76.02 mIoU. Our codes are available at
https://github.com/yzd-v/MGD.
