authors: Mingbao Lin, Rongrong Ji, etc. comments: IJCAL2020 cite: [2001.08565v3] Channel Pruning via Automatic Structure Search (arxiv.org) code: https://github.com/lmbxmu/ABCPruner (official) 0.Abstract In this paper, we propose a new channel prunin…

论文地址:https://arxiv.org/abs/1707.06168 代码地址:https://github.com/yihui-he/channel-pruning 采用方法 这篇文章主要讲诉了采用裁剪信道(channel pruning)的方法实现深度网络的加速.主要方法有两点: (1)LASSO regression based channel selection. (2)least square reconstruction. 实现效果 VGG-16实现5x的加速,0.3%误差增加…

论文地址 channel pruning是指给定一个CNN模型,去掉卷积层的某几个输入channel以及相应的卷积核, 并最小化裁剪channel后与原始输出的误差. 可以分两步来解决: channel selection 利用LASSO回归裁剪掉多余的channel,求出每个channel的权重,如果为0即是被裁减. feature map reconstruction 利用剩下的channel重建输出,直接使用最小平方误差来拟合原始卷积层的输出,求出新的卷积核W. 二.优化目标 2.1 定义…

[论文分享] DHP: Differentiable Meta Pruning via HyperNetworks authors: Yawei Li1, Shuhang Gu, etc. comments: ECCV2020 cite: [2003.13683] DHP: Differentiable Meta Pruning via HyperNetworks (arxiv.org) code: ofsoundof/dhp: This is the official implementati…

MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning 2019-08-11 19:48:17 Paper: https://arxiv.org/pdf/1903.10258.pdf Code: https://github.com/liuzechun/MetaPruning 1. Background and Motivation:…

DMCP 2020-CVPR-DMCP Differentiable Markov Channel Pruning for Neural Networks Shaopeng Guo(sensetime 商汤) GitHub: 64 stars https://github.com/zx55/dmcp Introduction propose a novel differentiable channel pruning method named Differentiable Markov Chan…

论文分享第四期-2019.04.16 Residual Attention Network for Image Classification,CVPR 2017,RAN 核心:将注意力机制与ResNet结合,用于图像分类.论文设计了一个注意力模块(Attention Module),通过级联该模块(即增加模型深度),网络可以学到细粒度的特征图谱(fined-grained feature maps),因为随着层数的加深,来自不同模块的注意力感知特征可以自适应地改变. 除了注意机制带来的更具判别性…

Refer to: http://stackoverflow.com/questions/10828294/c-and-c-partial-initialization-of-automatic-structure The points, The linked gcc documentation does not talk of Partial Initialization it just talks of (Complete)Initialization or No Initializatio…

论文分享第三期-2019.03.29 Fully convolutional networks for semantic segmentation,CVPR 2015,FCN 一.全连接层与全局平均池化 在介绍FCN网络的全卷积连接之前,先介绍一下全连接层(fully connected layers)和全局平均池化(global average pooling) 全连接层可以将前面的多层卷积学到的“分布式特征表示”(或者说是高层的鲁棒特征)映射到样本类别空间,与softmax组合具有“分类器”…

论文分享第二期-2019.03.26 NIPS2015,Spatial Transformer Networks,STN,空间变换网络…