Surpassing Human-Level Performance on ImageNet Classification ImageNet Classification
在該文章的兩大創新點：一個是PReLU,一個是權值初始化的方法。下面我們分別一一來看。 PReLU(paramter ReLU) 所謂的PRelu，即在 ReLU激活函數的基礎上加入了一個參數，看一個圖就明白了： 右邊的圖上多了一個參數吧，在負半軸上不再爲0，而是一個可以學習的斜率。 很容易明白。實驗結果顯示該方法可以提高識別率。 權值初始化的方法： 對於文中的權值初始化問題：主
相關文章
- 1. PReLU——Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
- 2. [Kaiming]Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
- 3. Pre-training on Grayscale ImageNet Improves Medical Image Classification
- 4. ImageNet Classification with Deep Convolutional Neural Networks
- 5. DenseNet Models for Tiny ImageNet Classification
- 6. 《ImageNet Classification with Deep Convolutional Neural Network》
- 7. 讀文獻——《ImageNet classification with deep convolutional neural networks》
- 8. 【Imagenet LSVRC系列模型】Alexnet-2012 Imagenet Classification with Deep Convolutionnal Neural Networks
- 9. ImageNet Classification with Deep Convolutional Neural Networks -- 解讀
- 10. AlexNet--ImageNet Classification with Deep Convolutional Neural Networks
- 更多相關文章...
- • R 包 - R 語言教程
- • ionic 手勢事件 - ionic 教程
- • RxJava操作符（一）Creating Observables
- • PHP Ajax 跨域問題最佳解決方案
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. PReLU——Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
- 2. [Kaiming]Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
- 3. Pre-training on Grayscale ImageNet Improves Medical Image Classification
- 4. ImageNet Classification with Deep Convolutional Neural Networks
- 5. DenseNet Models for Tiny ImageNet Classification
- 6. 《ImageNet Classification with Deep Convolutional Neural Network》
- 7. 讀文獻——《ImageNet classification with deep convolutional neural networks》
- 8. 【Imagenet LSVRC系列模型】Alexnet-2012 Imagenet Classification with Deep Convolutionnal Neural Networks
- 9. ImageNet Classification with Deep Convolutional Neural Networks -- 解讀
- 10. AlexNet--ImageNet Classification with Deep Convolutional Neural Networks