Diagonalwise Refactorization: An Efficient Training Method for Depthwise Convolutions筆記
論文地址：Diagonalwise Refactorization: An Efficient Training Method for Depthwise Convolutions 摘要 Depthwise Conv由於減少了參數和乘加運算因而具備顯着的性能優勢。然而，在當前的深度學習框架中，使用GPU進行Depthwise Conv訓練的速度很慢，因爲它們的實現不能充分利用GPU的能力。爲了解決
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相關文章
- 1. ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
- 2. 00036-Xception:Deep Learning with Depthwise Separable Convolutions
- 3. 論文閱讀筆記：ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
- 4. 論文筆記：ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
- 5. CondenseNet: An Efficient DenseNet using Learned Group Convolutions
- 6. [論文筆記]Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
- 7. 論文筆記-IGCV3：Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks
- 8. EffNet: An Efficient Structure for Convolutional Neural Networks
- 9. Paper5《An Efficient Pruning Method to Process Reverse Skyline Queries 》(2014)閱讀筆記
- 10. Reading Note: ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices