Y
Yunhe Wang
Researcher at Huawei
Publications - 177
Citations - 6399
Yunhe Wang is an academic researcher from Huawei. The author has contributed to research in topics: Computer science & Convolutional neural network. The author has an hindex of 27, co-authored 129 publications receiving 2525 citations. Previous affiliations of Yunhe Wang include Peking University.
Papers
More filters
Proceedings ArticleDOI
GhostNet: More Features From Cheap Operations
Abstract: 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 characteristic of those successful CNNs, but has rarely been investigated in neural architecture design. This paper proposes a novel Ghost module to generate more feature maps from cheap operations. Based on a set of intrinsic feature maps, we apply a series of linear transformations with cheap cost to generate many ghost feature maps that could fully reveal information underlying intrinsic features. The proposed Ghost module can be taken as a plug-and-play component to upgrade existing convolutional neural networks. Ghost bottlenecks are designed to stack Ghost modules, and then the lightweight GhostNet can be easily established. Experiments conducted on benchmarks demonstrate that the proposed Ghost module is an impressive alternative of convolution layers in baseline models, and our GhostNet can achieve higher recognition performance (e.g. 75.7% top-1 accuracy) than MobileNetV3 with similar computational cost on the ImageNet ILSVRC-2012 classification dataset. Code is available at https://github.com/huawei-noah/ghostnet.
Posted Content
GhostNet: More Features from Cheap Operations
TL;DR: A novel Ghost module is proposed to generate more feature maps from cheap operations based on a set of intrinsic feature maps to generate many ghost feature maps that could fully reveal information underlying intrinsic features.
Posted Content
Pre-Trained Image Processing Transformer
Hanting Chen,Yunhe Wang,Tianyu Guo,Chang Xu,Yiping Deng,Zhenhua Liu,Siwei Ma,Chunjing Xu,Chao Xu,Wen Gao +9 more
TL;DR: To maximally excavate the capability of transformer, the IPT model is presented to utilize the well-known ImageNet benchmark for generating a large amount of corrupted image pairs and the contrastive learning is introduced for well adapting to different image processing tasks.
Proceedings ArticleDOI
Pre-Trained Image Processing Transformer
Hanting Chen,Yunhe Wang,Tianyu Guo,Chang Xu,Yiping Deng,Zhenhua Liu,Siwei Ma,Chunjing Xu,Chao Xu,Wen Gao +9 more
TL;DR: Hu et al. as discussed by the authors proposed a pre-trained image processing transformer (IPT) model for denoising, super-resolution and deraining tasks, which is trained on corrupted image pairs with multi-heads and multi-tails.
Proceedings ArticleDOI
Data-Free Learning of Student Networks
Hanting Chen,Yunhe Wang,Chang Xu,Zhaohui Yang,Chuanjian Liu,Boxin Shi,Chunjing Xu,Chao Xu,Qi Tian +8 more
TL;DR: A novel framework for training efficient deep neural networks by exploiting generative adversarial networks (GANs) is proposed, where the pre-trained teacher networks are regarded as a fixed discriminator and the generator is utilized for derivating training samples which can obtain the maximum response on the discriminator.