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论文地址:https://openaccess.thecvf.com/content_cvpr_2018/papers/Hu_Squeeze-and-Excitation_Networks_CVPR_2018_paper.pdf SENet官方Caffe实现:https://github.com/hujie-frank/SENet 民间TensorFlow实现:https://github.com/taki0112/SENet-Tensorflow 民间PyTorch实现:https://github.com/moskomule/senet.pytorch...
論文標題:Squeeze-and-Excitation Networks 論文作者:Jie Hu Li Shen Gang Sun 論文地址:https://openaccess.thecvf.com/content_cvpr_2018/papers/Hu_Squeeze-and-Excitation_Networks_CVPR_2018_paper.pdf SENet官方Caffe實現:https://github.com/hujie-frank/SENet 民間TensorFlow實現:https://github.com/taki0112...
摘要原文 The central building block of convolutional neural networks (CNNs) is the convolution operator, which enables networks to construct informative features by fusing both spatial and channel-wise information within local receptive fields at each layer. A broad range of prior research has investig...
GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond论文阅读翻译,程序员大本营,技术文章内容聚合第一站。
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Squeeze-and-Excitation Networks论文翻译——中文版 Squeeze-and-Excitation Networks 摘要 卷积神经网络建立在卷积运算的基础上,通过融合局部感受野内的空间信息和通道信息来提取信息特征。为了提高网络的表示能力,许多现有的工作已经显示出增强空间编码的好处。在这项工作中,我们专注于通道,并提出了一种新颖的架构单元,...
CNNs are capable of capturing hierarchical patterns with global receptive fields as powerful image descriptions. Recent work has demonstrated the performance of networks can be improved by explicitly embedding learning mechanisms that help capture spatial correlations without requiring additional supervision. ...
翻译论文汇总:https://github.com/SnailTyan/deep-learning-papers-translation Squeeze-and-Excitation Networks Abstract Convolutional neural networks are built upon the convolution operation, which extracts informative features by fusing spatial and channel-wise information together within local receptive ...