18 citations · 33 across the 4 of their papers we have counts for
9 papers
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.…
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…
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…