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20182021
most citedConvolutional Neural Network Pruning with Structural Redundancy Reduction

14 citations · 18 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CV202114 cited

Convolutional Neural Network Pruning with Structural Redundancy Reduction

Zi Wang, Chengcheng Li, Xiangyang Wang

Convolutional neural network (CNN) pruning has become one of the most successful network compression approaches in recent years. Existing works on network pruning usually focus on…

cs.CV2019

Investigating Channel Pruning through Structural Redundancy Reduction -- A Statistical Study

Chengcheng Li, Zi Wang, Dali Wang +2

Most existing channel pruning methods formulate the pruning task from a perspective of inefficiency reduction which iteratively rank and remove the least important filters, or find…

cs.CV2019

Speeding up convolutional networks pruning with coarse ranking

Zi Wang, Chengcheng Li, Dali Wang +2

Channel-based pruning has achieved significant successes in accelerating deep convolutional neural network, whose pipeline is an iterative three-step procedure: ranking, pruning an…

cs.CV20194 cited

Single-shot Channel Pruning Based on Alternating Direction Method of Multipliers

Chengcheng Li, Zi Wang, Xiangyang Wang +1

Channel pruning has been identified as an effective approach to constructing efficient network structures. Its typical pipeline requires iterative pruning and fine-tuning. In this…

cs.CV2018

Fast-converging Conditional Generative Adversarial Networks for Image Synthesis

Chengcheng Li, Zi Wang, Hairong Qi

Building on top of the success of generative adversarial networks (GANs), conditional GANs attempt to better direct the data generation process by conditioning with certain additio…

q-bio.QM2018

Deep Reinforcement Learning of Cell Movement in the Early Stage of C. elegans Embryogenesis

Zi Wang, Dali Wang, Chengcheng Li +3

Cell movement in the early phase of C. elegans development is regulated by a highly complex process in which a set of rules and connections are formulated at distinct scales. Previ…