74 citations · 93 across the 8 of their papers we have counts for
8 papers
ShowFace: Coordinated Face Inpainting with Memory-Disentangled Refinement Networks
Zhuojie Wu, Xingqun Qi, Zijian Wang +4
Face inpainting aims to complete the corrupted regions of the face images, which requires coordination between the completed areas and the non-corrupted areas. Recently, memory-ori…
BlueFog: Make Decentralized Algorithms Practical for Optimization and Deep Learning
Bicheng Ying, Kun Yuan, Hanbin Hu +2
Decentralized algorithm is a form of computation that achieves a global goal through local dynamics that relies on low-cost communication between directly-connected agents. On larg…
Incorporating Convolution Designs into Visual Transformers
Kun Yuan, Shaopeng Guo, Ziwei Liu +3
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. How…
Differentiable Network Adaption with Elastic Search Space
Shaopeng Guo, Yujie Wang, Kun Yuan +1
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 adjus…
Learning N:M Fine-grained Structured Sparse Neural Networks From Scratch
Aojun Zhou, Yukun Ma, Junnan Zhu +5
Sparsity in Deep Neural Networks (DNNs) has been widely studied to compress and accelerate the models on resource-constrained environments. It can be generally categorized into uns…
Dynamic Graph: Learning Instance-aware Connectivity for Neural Networks
Kun Yuan, Quanquan Li, Dapeng Chen +2
One practice of employing deep neural networks is to apply the same architecture to all the input instances. However, a fixed architecture may not be representative enough for data…