1. Introduction Convolutional neural networks have been the main design paradigm for image understanding tasks, as initially demonstrated on image cl
Image clssification is so core to computer vision that it is often used as a benchmark to measure progress in image understanding.이미지 분류는 컴퓨터 비전의 핵심이기
In this section, we briefly recall preliminaries associated with the vision transformer 15, 52, and further discuss positional encoding and resolution
attention을 통한 증류In this section we assume we have access to a strong image classifier as a teacher model.이 섹션에서는 교사 모델로서 강력한 이미지 분류기에 액세스할 수 있다고 가정한다.
We verified that our distillation token adds something to the model, compared to simply adding an additional class token associated with the same targ
This section presents a few analytical experiments and results.present 제시하다이 섹션에서는 몇 가지 분석 실험과 결과를 제시합니다.We first discuss our distillation strategy. T
Although DeiT perform very well on ImageNet it is important to evaluate them on other datasets with transfer learning in order to measure the power of