  Knowledge Distillation (KD) utilizes training data as a transfer set to
transfer knowledge from a complex network (Teacher) to a smaller network
(Student). Several works have recently identified many scenarios where the
training data may not be available due to data privacy or sensitivity concerns
and have proposed solutions under this restrictive constraint for the
classification task. Unlike existing works, we, for the first time, solve a
much more challenging problem, i.e., "KD for object detection with zero
knowledge about the training data and its statistics". Our proposed approach
prepares pseudo-targets and synthesizes corresponding samples (termed as
"Multi-Object Impressions"), using only the pretrained Faster RCNN Teacher
network. We use this pseudo-dataset as a transfer set to conduct zero-shot KD
for object detection. We demonstrate the efficacy of our proposed method
through several ablations and extensive experiments on benchmark datasets like
KITTI, Pascal and COCO. Our approach with no training samples, achieves a
respectable mAP of 64.2% and 55.5% on the student with same and half capacity
while performing distillation from a Resnet-18 Teacher of 73.3% mAP on KITTI.
