DropNet: Reducing Neural Network Complexity via Iterative Pruning（論文閱讀）
目錄 說明 貢獻 動機 方法 實驗 所用模型 實驗1 MLP - MNIST 實驗2 CNN - MNIST 實驗3 CNN - CIFAR-10: Model C 實驗4 CNN - CIFAR-10: ResNet18/VGG19 實驗5 實例分析 - Oracle Comparison 實驗6 實例分析-Random Initialization 實驗7 實例分析：Percentage of
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相關文章
- 1. Rethinking the Value of Network Pruning(論文閱讀)
- 2. EagleEye: Fast Sub-net Evaluation for Efficient Neural Network Pruning（論文閱讀）
- 3. 論文閱讀《Edge Detection Using Convolutional Neural Network》
- 4. 【論文閱讀筆記】Relation Classification via Convolutional Deep Neural Network
- 5. 【論文閱讀筆記】——Batch Normalization:Accelerating Deep Network Traning by Reducing Internal Covariate Shift
- 6. Global Sparse Momentum SGD for Pruning Very Deep Neural Networks 論文閱讀筆記
- 7. 論文閱讀：Network in Network
- 8. Iterative Pruning
- 9. 論文--閱讀翻譯筆記-Reducing the Dimensionality of Data with Neural Networks
- 10. 論文閱讀 Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift