DINO(self-distillation with no label)首次将自监督学习与transformer结合.为结合卷积网络的局部性优势,提出一个三分支的网络模型DINO+,即在DINO的基础上添加一个卷积蒸馏模块,给DINO中的transformer进行知识蒸馏,从而将卷积网络与transformer结合.蒸馏后的ViT(vision transformer)在STL-10、CIFAR-10 数据集上的分类准确率分别上升了 5.7%、4.8%,且优于其他自监督模型,证明了提出方法的有效性.
Co-Distillation Image Classification Based on Improved DINO
DINO(self-distillation with no label)combines self-supervised learning with transformer for the first time.In order to combine the locality advantage of convolutional network,a three-branch network model named DINO+is proposed,which adds a convolution distillation module to DINO to dis-till knowledge to transformer in DINO,thus combining the convolutional network with transformer.The classification accuracy of the distilled ViT(vision transformer)on STL-10 and CIFAR-10 increases by 5.7%and 4.8%respectively,and is better than other self-supervised models,demonstrating the ef-fectiveness of the proposed method.
万方数据
维普
