activity
20172021
most citedGlobal Sparse Momentum SGD for Pruning Very Deep Neural Networks

125 citations · 409 across the 32 of their papers we have counts for

collaborators
Showing cs.CVShow all

8 papers · 1 filter

cs.CV20212 cited

Joint Channel and Weight Pruning for Model Acceleration on Moblie Devices

Tianli Zhao, Xi Sheryl Zhang, Wentao Zhu +4

For practical deep neural network design on mobile devices, it is essential to consider the constraints incurred by the computational resources and the inference latency in various…

cs.CV2021

GDP: Stabilized Neural Network Pruning via Gates with Differentiable Polarization

Yi Guo, Huan Yuan, Jianchao Tan +3

Model compression techniques are recently gaining explosive attention for obtaining efficient AI models for various real-time applications. Channel pruning is one important compres…

cs.CV20217 cited

Shifted Chunk Transformer for Spatio-Temporal Representational Learning

Xuefan Zha, Wentao Zhu, Tingxun Lv +2

Spatio-temporal representational learning has been widely adopted in various fields such as action recognition, video object segmentation, and action anticipation. Previous spatio-…

cs.CV2021

Hand Image Understanding via Deep Multi-Task Learning

Xiong Zhang, Hongsheng Huang, Jianchao Tan +5

Analyzing and understanding hand information from multimedia materials like images or videos is important for many real world applications and remains active in research community.…

cs.CV202021 cited

Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free

Haotao Wang, Tianlong Chen, Shupeng Gui +3

Adversarial training and its many variants substantially improve deep network robustness, yet at the cost of compromising standard accuracy. Moreover, the training process is heavy…

cs.CV2020

Neural Network Activation Quantization with Bitwise Information Bottlenecks

Xichuan Zhou, Kui Liu, Cong Shi +2

Recent researches on information bottleneck shed new light on the continuous attempts to open the black box of neural signal encoding. Inspired by the problem of lossy signal compr…