196 citations · 408 across the 12 of their papers we have counts for
5 papers · 1 filter
AdderNet: Do We Really Need Multiplications in Deep Learning?
Hanting Chen, Yunhe Wang, Chunjing Xu +4
Compared with cheap addition operation, multiplication operation is of much higher computation complexity. The widely-used convolutions in deep neural networks are exactly cross-co…
GhostNet: More Features from Cheap Operations
Kai Han, Yunhe Wang, Qi Tian +3
Deploying convolutional neural networks (CNNs) on embedded devices is difficult due to the limited memory and computation resources. The redundancy in feature maps is an important…
CARS: Continuous Evolution for Efficient Neural Architecture Search
Zhaohui Yang, Yunhe Wang, Xinghao Chen +5
Searching techniques in most of existing neural architecture search (NAS) algorithms are mainly dominated by differentiable methods for the efficiency reason. In contrast, we devel…
Co-Evolutionary Compression for Unpaired Image Translation
Han Shu, Yunhe Wang, Xu Jia +5
Generative adversarial networks (GANs) have been successfully used for considerable computer vision tasks, especially the image-to-image translation. However, generators in these n…
Data-Free Learning of Student Networks
Hanting Chen, Yunhe Wang, Chang Xu +6
Learning portable neural networks is very essential for computer vision for the purpose that pre-trained heavy deep models can be well applied on edge devices such as mobile phones…