Large-Margin Softmax Loss for Convolutional Neural Networks
Large-Margin Softmax Loss for Convolutional Neural Networks 在本文中，我們將軟最大損耗定義爲交叉熵損耗、軟最大函數和最後一個完全連接層的組合（見圖1）。在這種定義下，許多流行的CNN模型可以被視爲卷積特徵學習組件和softmax損失組件的組合，如圖1所示。 儘管它的流行，當前的軟最大損失並不明確鼓勵類內的緊密性和類間的可分離性。 我們的主
相關文章
- 1. Large-Margin Softmax Loss for Convolutional Neural Networks
- 2. softmax變種或增強1：Large-Margin Softmax Loss for Convolutional Neural Networks
- 3. 論文筆記 | Large-Margin Softmax Loss for Convolutional Neural Networks
- 4. Bag of Tricks for Convolutional Neural Networks
- 5. Convolutional Neural Networks: Application
- 6. CS231n Convolutional Neural Networks for Visual Recognition
- 7. Large-Margin Softmax Loss
- 8. Convolutional Neural Networks for Visual Recognition
- 9. 《Convolutional Neural Networks for Sentence Classification》
- 10. Recurrent Convolutional Neural Networks for Text Classification
- 更多相關文章...
- • Swift for 循環 - Swift 教程
- • Scala for循環 - Scala教程
- • PHP開發工具
- • C# 中 foreach 遍歷的用法
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. Large-Margin Softmax Loss for Convolutional Neural Networks
- 2. softmax變種或增強1：Large-Margin Softmax Loss for Convolutional Neural Networks
- 3. 論文筆記 | Large-Margin Softmax Loss for Convolutional Neural Networks
- 4. Bag of Tricks for Convolutional Neural Networks
- 5. Convolutional Neural Networks: Application
- 6. CS231n Convolutional Neural Networks for Visual Recognition
- 7. Large-Margin Softmax Loss
- 8. Convolutional Neural Networks for Visual Recognition
- 9. 《Convolutional Neural Networks for Sentence Classification》
- 10. Recurrent Convolutional Neural Networks for Text Classification