46 citations · 64 across the 3 of their papers we have counts for
5 papers · 1 filter
RepVGG: Making VGG-style ConvNets Great Again
Xiaohan Ding, Xiangyu Zhang, Ningning Ma +3
We present a simple but powerful architecture of convolutional neural network, which has a VGG-like inference-time body composed of nothing but a stack of 3x3 convolution and ReLU,…
Activate or Not: Learning Customized Activation
Ningning Ma, Xiangyu Zhang, Ming Liu +1
We present a simple, effective, and general activation function we term ACON which learns to activate the neurons or not. Interestingly, we find Swish, the recent popular NAS-searc…
Funnel Activation for Visual Recognition
Ningning Ma, Xiangyu Zhang, Jian Sun
We present a conceptually simple but effective funnel activation for image recognition tasks, called Funnel activation (FReLU), that extends ReLU and PReLU to a 2D activation by ad…
WeightNet: Revisiting the Design Space of Weight Networks
Ningning Ma, Xiangyu Zhang, Jiawei Huang +1
We present a conceptually simple, flexible and effective framework for weight generating networks. Our approach is general that unifies two current distinct and extremely effective…
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng +1
Currently, the neural network architecture design is mostly guided by the \emph{indirect} metric of computation complexity, i.e., FLOPs. However, the \emph{direct} metric, e.g., sp…