  Convolutional neural networks have a significant improvement in the accuracy
of Object detection. As convolutional neural networks become deeper, the
accuracy of detection is also obviously improved, and more floating-point
calculations are needed. Many researchers use the knowledge distillation method
to improve the accuracy of student networks by transferring knowledge from a
deeper and larger teachers network to a small student network, in object
detection. Most methods of knowledge distillation need to designed complex cost
functions and they are aimed at the two-stage object detection algorithm. This
paper proposes a clean and effective knowledge distillation method for the
one-stage object detection. The feature maps generated by teacher network and
student network are used as true samples and fake samples respectively, and
generate adversarial training for both to improve the performance of the
student network in one-stage object detection.
