196 citations · 408 across the 12 of their papers we have counts for
14 papers
Computation-efficient Deep Learning for Computer Vision: A Survey
Yulin Wang, Yizeng Han, Chaofei Wang +3
Over the past decade, deep learning models have exhibited considerable advancements, reaching or even exceeding human-level performance in a range of visual perception tasks. This…
Hybrid Distillation: Connecting Masked Autoencoders with Contrastive Learners
Bowen Shi, Xiaopeng Zhang, Yaoming Wang +5
Representation learning has been evolving from traditional supervised training to Contrastive Learning (CL) and Masked Image Modeling (MIM). Previous works have demonstrated their…
GhostNets on Heterogeneous Devices via Cheap Operations
Kai Han, Yunhe Wang, Chang Xu +4
Deploying convolutional neural networks (CNNs) on mobile devices is difficult due to the limited memory and computation resources. We aim to design efficient neural networks for he…
Differentiable Convolution Search for Point Cloud Processing
Xing Nie, Yongcheng Liu, Shaohong Chen +6
Exploiting convolutional neural networks for point cloud processing is quite challenging, due to the inherent irregular distribution and discrete shape representation of point clou…
Pixel Difference Networks for Efficient Edge Detection
Zhuo Su, Wenzhe Liu, Zitong Yu +5
Recently, deep Convolutional Neural Networks (CNNs) can achieve human-level performance in edge detection with the rich and abstract edge representation capacities. However, the hi…
Fast Batch Nuclear-norm Maximization and Minimization for Robust Domain Adaptation
Shuhao Cui, Shuhui Wang, Junbao Zhuo +3
Due to the domain discrepancy in visual domain adaptation, the performance of source model degrades when bumping into the high data density near decision boundary in target domain.…
Training Compact CNNs for Image Classification using Dynamic-coded Filter Fusion
Mingbao Lin, Bohong Chen, Fei Chao +1
The mainstream approach for filter pruning is usually either to force a hard-coded importance estimation upon a computation-heavy pretrained model to select "important" filters, or…
A Semi-Supervised Assessor of Neural Architectures
Yehui Tang, Yunhe Wang, Yixing Xu +6
Neural architecture search (NAS) aims to automatically design deep neural networks of satisfactory performance. Wherein, architecture performance predictor is critical to efficient…
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…
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…