DropNet: Reducing Neural Network Complexity via Iterative Pruning（论文阅读）
目录 说明 贡献 动机 方法 实验 所用模型 实验1 MLP - MNIST 实验2 CNN - MNIST 实验3 CNN - CIFAR-10: Model C 实验4 CNN - CIFAR-10: ResNet18/VGG19 实验5 实例分析 - Oracle Comparison 实验6 实例分析-Random Initialization 实验7 实例分析：Percentage of
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相关文章
- 1. Rethinking the Value of Network Pruning(论文阅读)
- 2. EagleEye: Fast Sub-net Evaluation for Efficient Neural Network Pruning（论文阅读）
- 3. 论文阅读《Edge Detection Using Convolutional Neural Network》
- 4. 【论文阅读笔记】Relation Classification via Convolutional Deep Neural Network
- 5. 【论文阅读笔记】——Batch Normalization:Accelerating Deep Network Traning by Reducing Internal Covariate Shift
- 6. Global Sparse Momentum SGD for Pruning Very Deep Neural Networks 论文阅读笔记
- 7. 论文阅读：Network in Network
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- 9. 论文--阅读翻译笔记-Reducing the Dimensionality of Data with Neural Networks
- 10. 论文阅读 Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift