activity
20152020
most citedShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

892 citations · 1.2k across the 5 of their papers we have counts for

collaborators

8 papers

stat.ML202027 cited

Feature Space Singularity for Out-of-Distribution Detection

Haiwen Huang, Zhihan Li, Lulu Wang +3

Out-of-Distribution (OoD) detection is important for building safe artificial intelligence systems. However, current OoD detection methods still cannot meet the performance require…

stat.ME2020

Component-wise Adaptive Trimming For Robust Mixture Regression

Wennan Chang, Xinyu Zhou, Yong Zang +2

Parameter estimation of mixture regression model using the expectation maximization (EM) algorithm is highly sensitive to outliers. Here we propose a fast and efficient robust mixt…

cs.CV2020

DPGN: Distribution Propagation Graph Network for Few-shot Learning

Ling Yang, Liangliang Li, Zilun Zhang +3

Most graph-network-based meta-learning approaches model instance-level relation of examples. We extend this idea further to explicitly model the distribution-level relation of one…

cs.CV2020

Learning Delicate Local Representations for Multi-Person Pose Estimation

Yuanhao Cai, Zhicheng Wang, Zhengxiong Luo +7

In this paper, we propose a novel method called Residual Steps Network (RSN). RSN aggregates features with the same spatial size (Intra-level features) efficiently to obtain delica…

cs.LG201717 cited

Learning to Run with Actor-Critic Ensemble

Zhewei Huang, Shuchang Zhou, BoEr Zhuang +1

We introduce an Actor-Critic Ensemble(ACE) method for improving the performance of Deep Deterministic Policy Gradient(DDPG) algorithm. At inference time, our method uses a critic e…

cs.CV2017892 cited

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin +1

We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e.g., 10-150 MF…