no code implementations • 30 Mar 2021 • Shaopeng Guo, Yujie Wang, Kun Yuan, Quanquan Li
In this paper we propose a novel network adaption method called Differentiable Network Adaption (DNA), which can adapt an existing network to a specific computation budget by adjusting the width and depth in a differentiable manner.
3 code implementations • ICCV 2021 • Kun Yuan, Shaopeng Guo, Ziwei Liu, Aojun Zhou, Fengwei Yu, Wei Wu
Motivated by the success of Transformers in natural language processing (NLP) tasks, there emerge some attempts (e. g., ViT and DeiT) to apply Transformers to the vision domain.
Ranked #2 on Image Classification on Oxford-IIIT Pets
no code implementations • ICCV 2021 • Kun Yuan, Quanquan Li, Shaopeng Guo, Dapeng Chen, Aojun Zhou, Fengwei Yu, Ziwei Liu
A standard practice of deploying deep neural networks is to apply the same architecture to all the input instances.
1 code implementation • CVPR 2020 • Shaopeng Guo, Yujie Wang, Quanquan Li, Junjie Yan
In DMCP, we model the channel pruning as a Markov process, in which each state represents for retaining the corresponding channel during pruning, and transitions between states denote the pruning process.