activity
20142020
most citedPruning Filters for Efficient ConvNets

682 citations · 771 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG202013 cited

WeMix: How to Better Utilize Data Augmentation

Yi Xu, Asaf Noy, Ming Lin +3

Data augmentation is a widely used training trick in deep learning to improve the network generalization ability. Despite many encouraging results, several recent studies did point…

cs.CV20202 cited

Semi-Anchored Detector for One-Stage Object Detection

Lei Chen, Qi Qian, Hao Li

A standard one-stage detector is comprised of two tasks: classification and regression. Anchors of different shapes are introduced for each location in the feature map to mitigate…

cs.CV202074 cited

Learning to Generate Diverse Dance Motions with Transformer

Jiaman Li, Yihang Yin, Hang Chu +4

With the ongoing pandemic, virtual concerts and live events using digitized performances of musicians are getting traction on massive multiplayer online worlds. However, well chore…

cs.CV2016682 cited

Pruning Filters for Efficient ConvNets

Hao Li, Asim Kadav, Igor Durdanovic +2

The success of CNNs in various applications is accompanied by a significant increase in the computation and parameter storage costs. Recent efforts toward reducing these overheads…

cs.CE2014

Application of Multilayer Feedforward Neural Networks in Predicting Tree Height and Forest Stock Volume of Chinese Fir

Xiaohui Huang, Xing Hu, Weichang Jiang +2

Wood increment is critical information in forestry management. Previous studies used mathematics models to describe complex growing pattern of forest stand, in order to determine t…