EffNet: An Efficient Structure for Convolutional Neural Networks
EffeNet對MoblieNet網絡進行改進,主要思想爲: 首先,將MoblieNet的 3×3 的depthwise convolution層分解爲兩個 3×1 , 1×3 depthwise convolution層.這樣便可以在第一層之後就採用pool操作,從而減少第二層的計算量. 如圖1所示,在第一個卷積層之後,使用 1×2 max pooling操作.在第二個卷積層之後,用 2×1 ,
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相關文章
- 1. 【論文解讀】EffNet: AN EFFICIENT STRUCTURE FOR CONVOLUTIONAL NEURAL NETWORKS
- 2. Paper Reading：MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
- 3. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
- 4. ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
- 5. Pruning Convolutional Neural Networks For Resource Efficient Inference
- 6. 論文筆記：An Efficient Hardware Accelerator for Structured Sparse Convolutional Neural Networks on FPGAs
- 7. 論文筆記：An Efficient Hardware Accelerator for Sparse Convolutional Neural Networks on FPGAs
- 8. Understanding Convolutional Neural Networks for NLP
- 9. [轉] Understanding Convolutional Neural Networks for NLP
- 10. Reading Note: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications