Double Reverse Regularization Network Based on Self-Knowledge
  Distillation for SAR Object Classification
Bo Xu Hao Zheng Zhigang Hu Liu Yang Meiguang Zheng
In current synthetic aperture radar (SAR) object classification, one of the
major challenges is the severe overfitting issue due to the limited dataset
(few-shot) and noisy data. Considering the advantages of knowledge distillation
as a learned label smoothing regularization, this paper proposes a novel Double
Reverse Regularization Network based on Self-Knowledge Distillation
(DRRNet-SKD). Specifically, through exploring the effect of distillation weight
on the process of distillation, we are inspired to adopt the double reverse
thought to implement an effective regularization network by combining offline
and online distillation in a complementary way. Then, the Adaptive Weight
Assignment (AWA) module is designed to adaptively assign two reverse-changing
weights based on the network performance, allowing the student network to
better benefit from both teachers. The experimental results on OpenSARShip and
FUSAR-Ship demonstrate that DRRNet-SKD exhibits remarkable performance
improvement on classical CNNs, outperforming state-of-the-art self-knowledge
distillation methods.