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Channel Pruning for Accelerating Very Deep Neural Networks 相关文章
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Channel Pruning for Accelerating Very Deep Neural Networks
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Channel Pruning for Accelerating Very Deep Neural Networks
2020-12-30
模型压缩和剪枝
代码开源:Channel Pruning for Accelerating Very Deep Neural Networks
2020-12-30
cnn
开源
Channel Pruning for Accelerating Very Deep Neural Networks论文笔记
2020-12-30
【模型压缩】Channel Pruning for Accelerating Very Deep Neural Networks算法笔记
2020-12-30
论文笔记——Channel Pruning for Accelerating Very Deep Neural Networks
2019-12-08
论文
笔记
channel
pruning
accelerating
deep
neural
networks
Channel Pruning for Accelerating Very Deep Neural Networks 论文笔记
2020-12-30
模型压缩
《Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks》论文笔记
2020-12-30
深度学习算法相关
Global Sparse Momentum SGD for Pruning Very Deep Neural Networks 论文阅读笔记
2020-12-30
论文阅读
【Comprssion】《Deep Compression:Compressing Deep Neural Networks with Pruning,Trained QuantizationXXX》
2020-12-23
CNN
Very Deep Convolutional Networks for Text Classification
2020-12-24
文本分类
HTML
《Image Super-Resolution Using Very Deep Residual Channel Attention Networks》
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阅读论文
DeepID3: Face Recognition with Very Deep Neural Networks
2020-12-30
人脸识别
深度学习
Deep Neural Networks for Object Detection
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VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION
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卷积神经网络
CSS
Very Deep Convolutional Networks for Large-Scale Image Recognition
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CSS
Improving Deep Neural Networks
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通道剪枝Channel Pruning
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论文笔记——Data-free Parameter Pruning for Deep Neural Networks
2019-12-11
论文
笔记
data
free
parameter
pruning
deep
neural
networks
《FPGM:Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration》论文笔记
2020-12-29
[9] 模型压缩&加速
FPGM
论文笔记:Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration
2020-12-29
深度学习
模型压缩
滤波器剪枝
[CV Paper] A practical theory for designing very deep convolutional neural networks
2021-01-02
Pruning Convolutional Neural Networks For Resource Efficient Inference
2020-12-23
Pruning convolutional neural networks for resource efficent inference
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Aggregated Residual Transformations for Deep Neural Networks
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