DropBlock： A regularization method for convolutional networks
arxiv上一篇新文章，講如何在卷積層進行dropout來防止網絡過擬合。 原來的dropout方法多是作用在全連接層上，在卷積層應用dropout方法意義不大。文章認爲是因爲每個featuremap的位置都有一個感受野範圍，僅僅對單個像素位置進行dropout並不能降低featuremap學習的特徵範圍，也就是說網絡仍可以通過該位置的相鄰位置元素去學習對應的語義信息，也就不會促使網絡去學習更加魯
相關文章
- 1. 《DropBlock: A regularization method for convolutional networks》筆記
- 2. （原）DropBlock A regularization method for convolutional networks
- 3. DropBlock: A regularization method for convolutional networks論文解讀
- 4. Regularization method for machine learning
- 5. Convolutional Neural Networks for Sentence Classification
- 6. Knowledge Graph Convolutional Networks for Recommender Systems with Label Smoothness Regularization
- 7. Bag of Tricks for Convolutional Neural Networks
- 8. 神經網絡regularization技巧 dropbock
- 9. Understanding Convolutional Neural Networks for NLP
- 10. [轉] Understanding Convolutional Neural Networks for NLP
- 更多相關文章...
- • Scala for循環 - Scala教程
- • Lua for 循環 - Lua 教程
- • ☆基於Java Instrument的Agent實現
- • 爲了進字節跳動，我精選了29道Java經典算法題，帶詳細講解
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。
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》筆記
- 2. （原）DropBlock A regularization method for convolutional networks
- 3. DropBlock: A regularization method for convolutional networks論文解讀
- 4. Regularization method for machine learning
- 5. Convolutional Neural Networks for Sentence Classification
- 6. Knowledge Graph Convolutional Networks for Recommender Systems with Label Smoothness Regularization
- 7. Bag of Tricks for Convolutional Neural Networks
- 8. 神經網絡regularization技巧 dropbock
- 9. Understanding Convolutional Neural Networks for NLP
- 10. [轉] Understanding Convolutional Neural Networks for NLP