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chenfu 发起了问题 • 1 人关注 • 0 个回复 • 149 次浏览 • 2017-10-28 23:24 • 来自相关话题

请问AlexNet论文中的Local Response Normalization目前还在使用吗?

Paper 回复了问题 • 2 人关注 • 1 个回复 • 197 次浏览 • 2017-09-08 09:44 • 来自相关话题

【ICCV 2017】Interleaved Group Convolutions for Deep Neural Networks

Paper 发表了文章 • 0 个评论 • 598 次浏览 • 2017-08-11 11:27 • 来自相关话题

关键词:缩减模型,性能无损,交错组卷积
 
Interleaved Group Convolutions for Deep Neural Networks
 
Ting Zhang, Guo-Jun Qi, Bin Xiao, Jingdong Wang
 
ICCV 2017
 
paper: https://arxiv.org/abs/1707.02725
中文翻译: http://www.sohu.com/a/161110049_465975
 
In this paper, we present a simple and modularized neural network architecture, named interleaved group convolutional neural networks (IGCNets). The main point lies in a novel building block, a pair of two successive interleaved group convolutions: primary group convolution and secondary group convolution. The two group convolutions are complementary: (i) the convolution on each partition in primary group convolution is a spatial convolution, while on each partition in secondary group convolution, the convolution is a point-wise convolution; (ii) the channels in the same secondary partition come from different primary partitions. We discuss one representative advantage: Wider than a regular convolution with the number of parameters and the computation complexity preserved. We also show that regular convolutions, group convolution with summation fusion, and the Xception block are special cases of interleaved group convolutions. Empirical results over standard benchmarks, CIFAR-10 , CIFAR-100 , SVHN and ImageNet demonstrate that our networks are more efficient in using parameters and computation complexity with similar or higher accuracy.
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关键词:缩减模型,性能无损,交错组卷积
 
Interleaved Group Convolutions for Deep Neural Networks
 
Ting Zhang, Guo-Jun Qi, Bin Xiao, Jingdong Wang
 
ICCV 2017
 
paper: https://arxiv.org/abs/1707.02725
中文翻译: http://www.sohu.com/a/161110049_465975
 
In this paper, we present a simple and modularized neural network architecture, named interleaved group convolutional neural networks (IGCNets). The main point lies in a novel building block, a pair of two successive interleaved group convolutions: primary group convolution and secondary group convolution. The two group convolutions are complementary: (i) the convolution on each partition in primary group convolution is a spatial convolution, while on each partition in secondary group convolution, the convolution is a point-wise convolution; (ii) the channels in the same secondary partition come from different primary partitions. We discuss one representative advantage: Wider than a regular convolution with the number of parameters and the computation complexity preserved. We also show that regular convolutions, group convolution with summation fusion, and the Xception block are special cases of interleaved group convolutions. Empirical results over standard benchmarks, CIFAR-10 , CIFAR-100 , SVHN and ImageNet demonstrate that our networks are more efficient in using parameters and computation complexity with similar or higher accuracy.
 

【CVPR 2017 BEST PAPER】Densely Connected Convolutional Netoworks

Paper 发表了文章 • 0 个评论 • 365 次浏览 • 2017-08-07 16:57 • 来自相关话题

 
Densely Connected Convolutional Networks
 
Gao Huang*, Zhuang Liu*, Laurens van der Maaten and Kilian Weinberger
 
CVPR 2017 BEST PAPER
 
arXiv: https://arxiv.org/abs/1608.06993
code: https://github.com/liuzhuang13/DenseNet
 
Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections - one between each layer and its subsequent layer - our network has L(L+1)/2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet). DenseNets obtain significant improvements over the state-of-the-art on most of them, whilst requiring less memory and computation to achieve high performance. Code and models are available at this https URL .
 
  查看全部
 
Densely Connected Convolutional Networks
 
Gao Huang*, Zhuang Liu*, Laurens van der Maaten and Kilian Weinberger
 
CVPR 2017 BEST PAPER
 
arXiv: https://arxiv.org/abs/1608.06993
code: https://github.com/liuzhuang13/DenseNet
 
Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections - one between each layer and its subsequent layer - our network has L(L+1)/2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet). DenseNets obtain significant improvements over the state-of-the-art on most of them, whilst requiring less memory and computation to achieve high performance. Code and models are available at this https URL .