Knowledge Distillation for Object Detection: from generic to remote
  sensing datasets
Hoàng-Ân Lê Minh-Tan Pham
Knowledge distillation, a well-known model compression technique, is an
active research area in both computer vision and remote sensing communities. In
this paper, we evaluate in a remote sensing context various off-the-shelf
object detection knowledge distillation methods which have been originally
developed on generic computer vision datasets such as Pascal VOC. In
particular, methods covering both logit mimicking and feature imitation
approaches are applied for vehicle detection using the well-known benchmarks
such as xView and VEDAI datasets. Extensive experiments are performed to
compare the relative performance and interrelationships of the methods.
Experimental results show high variations and confirm the importance of result
aggregation and cross validation on remote sensing datasets.