DearKD: Data-Efficient Early Knowledge Distillation for Vision Transformers
Xianing Chen, Qiong Cao, Yujie Zhong, Jing Zhang, Shenghua Gao, Dacheng Tao
Transformers have been successfully applied to computer vision due to its powerful modelling capacity with self-attention. However, the good performance of transformers heavily depends on enormous training images. Thus, a data-efficient transformer solution is urgently needed. In this work, we propose an early knowledge distillation framework, which is termed as DearKD, to improvethe data-efficiency required by transformers. Our DearKD is a two-stage framework that first distills the inductive biases from the early intermediate layers of a CNN and then gives the transformer full play by training without distillation. Further, our DearKD can also be applied to the extreme data-free case where no real images are available, where we propose a boundary-preserving intra-divergence loss based on DeepInversion to further close the performance gap against the full-data counterpart. Extensive experiments on ImageNet, partial ImageNet, data-free setting and other downstream tasks prove the superiority of DearKD over its baselines and state-of-the-art methods.
