  Knowledge distillation has shown great success in classification, however, it
is still challenging for detection. In a typical image for detection,
representations from different locations may have different contributions to
detection targets, making the distillation hard to balance. In this paper, we
propose a conditional distillation framework to distill the desired knowledge,
namely knowledge that is beneficial in terms of both classification and
localization for every instance. The framework introduces a learnable
conditional decoding module, which retrieves information given each target
instance as query. Specifically, we encode the condition information as query
and use the teacher's representations as key. The attention between query and
key is used to measure the contribution of different features, guided by a
localization-recognition-sensitive auxiliary task. Extensive experiments
demonstrate the efficacy of our method: we observe impressive improvements
under various settings. Notably, we boost RetinaNet with ResNet-50 backbone
from 37.4 to 40.7 mAP (+3.3) under 1x schedule, that even surpasses the teacher
(40.4 mAP) with ResNet-101 backbone under 3x schedule. Code has been released
on https://github.com/megvii-research/ICD.
