125 citations · 339 across the 8 of their papers we have counts for
5 papers · 1 filter
Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs
Xiaohan Ding, Xiangyu Zhang, Yizhuang Zhou +3
We revisit large kernel design in modern convolutional neural networks (CNNs). Inspired by recent advances in vision transformers (ViTs), in this paper, we demonstrate that using a…
Manipulating Identical Filter Redundancy for Efficient Pruning on Deep and Complicated CNN
Xiaohan Ding, Tianxiang Hao, Jungong Han +2
The existence of redundancy in Convolutional Neural Networks (CNNs) enables us to remove some filters/channels with acceptable performance drops. However, the training objective of…
Diverse Branch Block: Building a Convolution as an Inception-like Unit
Xiaohan Ding, Xiangyu Zhang, Jungong Han +1
We propose a universal building block of Convolutional Neural Network (ConvNet) to improve the performance without any inference-time costs. The block is named Diverse Branch Block…
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,…
ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks
Xiaohan Ding, Yuchen Guo, Guiguang Ding +1
As designing appropriate Convolutional Neural Network (CNN) architecture in the context of a given application usually involves heavy human works or numerous GPU hours, the researc…