56 citations · 164 across the 24 of their papers we have counts for
6 papers · 1 filter
Exploiting Operation Importance for Differentiable Neural Architecture Search
Xukai Xie, Yuan Zhou, Sun-Yuan Kung
Recently, differentiable neural architecture search methods significantly reduce the search cost by constructing a super network and relax the architecture representation by assign…
Cross-Scale Residual Network for Multiple Tasks:Image Super-resolution, Denoising, and Deblocking
Yuan Zhou, Xiaoting Du, Yeda Zhang +1
In general, image restoration involves mapping from low quality images to their high-quality counterparts. Such optimal mapping is usually non-linear and learnable by machine learn…
Temporal Action Localization using Long Short-Term Dependency
Yuan Zhou, Hongru Li, Sun-Yuan Kung
Temporal action localization in untrimmed videos is an important but difficult task. Difficulties are encountered in the application of existing methods when modeling temporal stru…
Comb Convolution for Efficient Convolutional Architecture
Dandan Li, Yuan Zhou, Shuwei Huo +1
Convolutional neural networks (CNNs) are inherently suffering from massively redundant computation (FLOPs) due to the dense connection pattern between feature maps and convolution…
Scalable Kernel Learning via the Discriminant Information
Mert Al, Zejiang Hou, Sun-Yuan Kung
Kernel approximation methods create explicit, low-dimensional kernel feature maps to deal with the high computational and memory complexity of standard techniques. This work studie…
HGC: Hierarchical Group Convolution for Highly Efficient Neural Network
Xukai Xie, Yuan Zhou, Sun-Yuan Kung
Group convolution works well with many deep convolutional neural networks (CNNs) that can effectively compress the model by reducing the number of parameters and computational cost…