activity
20182020
most citedPositive-Unlabeled Compression on the Cloud

23 citations · 30 across the 3 of their papers we have counts for

collaborators

6 papers

cs.CV2020

Distilling portable Generative Adversarial Networks for Image Translation

Hanting Chen, Yunhe Wang, Han Shu +5

Despite Generative Adversarial Networks (GANs) have been widely used in various image-to-image translation tasks, they can be hardly applied on mobile devices due to their heavy co…

cs.CV20201 cited

Widening and Squeezing: Towards Accurate and Efficient QNNs

Chuanjian Liu, Kai Han, Yunhe Wang +3

Quantization neural networks (QNNs) are very attractive to the industry because their extremely cheap calculation and storage overhead, but their performance is still worse than th…

cs.LG201923 cited

Positive-Unlabeled Compression on the Cloud

Yixing Xu, Yunhe Wang, Hanting Chen +4

Many attempts have been done to extend the great success of convolutional neural networks (CNNs) achieved on high-end GPU servers to portable devices such as smart phones. Providin…

cs.CV2019

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…

cs.LG2019

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…

cs.LG20186 cited

Learning Student Networks via Feature Embedding

Hanting Chen, Yunhe Wang, Chang Xu +2

Deep convolutional neural networks have been widely used in numerous applications, but their demanding storage and computational resource requirements prevent their applications on…