《DropBlock: A regularization method for convolutional networks》筆記
Introduction Dropout的思想是隨機失活一部分激活單元，讓輸出的feature或feature map丟失一些信息，使得網絡能夠關注更多的有辨別能力的特徵，而不是隻關注某幾個特徵，從而使得網絡更加魯棒，從另一方面來說，dropout起到了正則化的作用。 Dropout應用到卷積網絡的feature map上，具體操作是在每個feature map上隨機失活部分神經元。DropBlo
相關文章
- 1. DropBlock： A regularization method for convolutional networks
- 2. （原）DropBlock A regularization method for convolutional networks
- 3. DropBlock: A regularization method for convolutional networks論文解讀
- 4. HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs--論文筆記
- 5. Regularization method for machine learning
- 6. 【論文筆記】Fully Convolutional Networks for Semantic Segmentation
- 7. Deformable Convolutional Networks筆記
- 8. Learning Convolutional Neural Networks for Graphs論文筆記
- 9. 學習筆記：Fully Convolutional Networks for Segmantic Segmentation
- 10. Improved Regularization of Convolutional Neural Networks with Cutout論文筆記
- 更多相關文章...
- • Scala for循環 - Scala教程
- • ASP.NET Razor - 標記 - ASP.NET 教程
- • Tomcat學習筆記（史上最全tomcat學習筆記）
- • ☆基於Java Instrument的Agent實現
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
最新文章
歡迎關注本站公眾號,獲取更多信息
相關文章
- 1. DropBlock： A regularization method for convolutional networks
- 2. （原）DropBlock A regularization method for convolutional networks
- 3. DropBlock: A regularization method for convolutional networks論文解讀
- 4. HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs--論文筆記
- 5. Regularization method for machine learning
- 6. 【論文筆記】Fully Convolutional Networks for Semantic Segmentation
- 7. Deformable Convolutional Networks筆記
- 8. Learning Convolutional Neural Networks for Graphs論文筆記
- 9. 學習筆記：Fully Convolutional Networks for Segmantic Segmentation
- 10. Improved Regularization of Convolutional Neural Networks with Cutout論文筆記