  Knowledge distillation is a widely used paradigm for inheriting information
from a complicated teacher network to a compact student network and maintaining
the strong performance. Different from image classification, object detectors
are much more sophisticated with multiple loss functions in which features that
semantic information rely on are tangled. In this paper, we point out that the
information of features derived from regions excluding objects are also
essential for distilling the student detector, which is usually ignored in
existing approaches. In addition, we elucidate that features from different
regions should be assigned with different importance during distillation. To
this end, we present a novel distillation algorithm via decoupled features
(DeFeat) for learning a better student detector. Specifically, two levels of
decoupled features will be processed for embedding useful information into the
student, i.e., decoupled features from neck and decoupled proposals from
classification head. Extensive experiments on various detectors with different
backbones show that the proposed DeFeat is able to surpass the state-of-the-art
distillation methods for object detection. For example, DeFeat improves
ResNet50 based Faster R-CNN from 37.4% to 40.9% mAP, and improves ResNet50
based RetinaNet from 36.5% to 39.7% mAP on COCO benchmark. Our implementation
is available at https://github.com/ggjy/DeFeat.pytorch.
