most citedWeight-Sharing Neural Architecture Search: A Battle to Shrink the Optimization Gap

28 citations · 28 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CV2020

SelectScale: Mining More Patterns from Images via Selective and Soft Dropout

Zhengsu Chen, Jianwei Niu, Xuefeng Liu +1

Convolutional neural networks (CNNs) have achieved remarkable success in image recognition. Although the internal patterns of the input images are effectively learned by the CNNs,…

cs.CV202028 cited

Weight-Sharing Neural Architecture Search: A Battle to Shrink the Optimization Gap

Lingxi Xie, Xin Chen, Kaifeng Bi +8

Neural architecture search (NAS) has attracted increasing attentions in both academia and industry. In the early age, researchers mostly applied individual search methods which sam…

cs.CV2020

Network Adjustment: Channel Search Guided by FLOPs Utilization Ratio

Zhengsu Chen, Jianwei Niu, Lingxi Xie +3

Automatic designing computationally efficient neural networks has received much attention in recent years. Existing approaches either utilize network pruning or leverage the networ…

eess.IV2020

A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis

Xiaozheng Xie, Jianwei Niu, Xuefeng Liu +3

Although deep learning models like CNNs have achieved great success in medical image analysis, the small size of medical datasets remains a major bottleneck in this area. To addres…

cs.CV2018

DropFilter: Dropout for Convolutions

Zhengsu Chen Jianwei Niu Qi Tian

Using a large number of parameters , deep neural networks have achieved remarkable performance on computer vison and natural language processing tasks. However the networks usually…