28 citations · 31 across the 4 of their papers we have counts for
6 papers
Angle-based Search Space Shrinking for Neural Architecture Search
Yiming Hu, Yuding Liang, Zichao Guo +5
In this work, we present a simple and general search space shrinking method, called Angle-Based search space Shrinking (ABS), for Neural Architecture Search (NAS). Our approach pro…
Cluster Regularized Quantization for Deep Networks Compression
Yiming Hu, Jianquan Li, Xianlei Long +4
Deep neural networks (DNNs) have achieved great success in a wide range of computer vision areas, but the applications to mobile devices is limited due to their high storage and co…
Multi-loss-aware Channel Pruning of Deep Networks
Yiming Hu, Siyang Sun, Jianquan Li +3
Channel pruning, which seeks to reduce the model size by removing redundant channels, is a popular solution for deep networks compression. Existing channel pruning methods usually…
Better Guider Predicts Future Better: Difference Guided Generative Adversarial Networks
Guohao Ying, Yingtian Zou, Lin Wan +2
Predicting the future is a fantasy but practicality work. It is the key component to intelligent agents, such as self-driving vehicles, medical monitoring devices and robotics. In…
Action Machine: Rethinking Action Recognition in Trimmed Videos
Jiagang Zhu, Wei Zou, Liang Xu +6
Existing methods in video action recognition mostly do not distinguish human body from the environment and easily overfit the scenes and objects. In this work, we present a concept…
A novel channel pruning method for deep neural network compression
Yiming Hu, Siyang Sun, Jianquan Li +2
In recent years, deep neural networks have achieved great success in the field of computer vision. However, it is still a big challenge to deploy these deep models on resource-cons…