144 citations · 1.1k across the 52 of their papers we have counts for
8 papers · 2 filters
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…
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…
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.…
Bilinear Graph Networks for Visual Question Answering
Dalu Guo, Chang Xu, Dacheng Tao
This paper revisits the bilinear attention networks in the visual question answering task from a graph perspective. The classical bilinear attention networks build a bilinear atten…
Gated-GAN: Adversarial Gated Networks for Multi-Collection Style Transfer
Xinyuan Chen, Chang Xu, Xiaokang Yang +2
Style transfer describes the rendering of an image semantic content as different artistic styles. Recently, generative adversarial networks (GANs) have emerged as an effective appr…