4.4k citations · 7.7k across the 83 of their papers we have counts for
13 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…
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…
ReNAS:Relativistic Evaluation of Neural Architecture Search
Yixing Xu, Yunhe Wang, Kai Han +4
An effective and efficient architecture performance evaluation scheme is essential for the success of Neural Architecture Search (NAS). To save computational cost, most of existing…
Searching for Accurate Binary Neural Architectures
Mingzhu Shen, Kai Han, Chunjing Xu +1
Binary neural networks have attracted tremendous attention due to the efficiency for deploying them on mobile devices. Since the weak expression ability of binary weights and featu…
Balanced Binary Neural Networks with Gated Residual
Mingzhu Shen, Xianglong Liu, Ruihao Gong +1
Binary neural networks have attracted numerous attention in recent years. However, mainly due to the information loss stemming from the biased binarization, how to preserve the acc…
Efficient Residual Dense Block Search for Image Super-Resolution
Dehua Song, Chang Xu, Xu Jia +3
Although remarkable progress has been made on single image super-resolution due to the revival of deep convolutional neural networks, deep learning methods are confronted with the…
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…
Full-Stack Filters to Build Minimum Viable CNNs
Kai Han, Yunhe Wang, Yixing Xu +3
Deep convolutional neural networks (CNNs) are usually over-parameterized, which cannot be easily deployed on edge devices such as mobile phones and smart cameras. Existing works us…
Learning Instance-wise Sparsity for Accelerating Deep Models
Chuanjian Liu, Yunhe Wang, Kai Han +2
Exploring deep convolutional neural networks of high efficiency and low memory usage is very essential for a wide variety of machine learning tasks. Most of existing approaches use…
Attribute Aware Pooling for Pedestrian Attribute Recognition
Kai Han, Yunhe Wang, Han Shu +3
This paper expands the strength of deep convolutional neural networks (CNNs) to the pedestrian attribute recognition problem by devising a novel attribute aware pooling algorithm.…
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…