PReLU——Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
1. 摘要 在 R e L U ReLU ReLU 的基礎上作者提出了 P R e L U PReLU PReLU，在幾乎沒有增加額外參數的前提下既可以提升模型的擬合能力，又能減小過擬合風險。 針對 R e L U / P R e L U ReLU/PReLU ReLU/PReLU 的矯正非線性，作者設計了一個魯棒的的參數初始化方法。 2. 介紹 在過去幾年，隨着更強大網絡模型的構建和有效防止過擬
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相關文章
- 1. [Kaiming]Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
- 2. Surpassing Human-Level Performance on ImageNet Classification ImageNet Classification
- 3. ImageNet Classification with Deep Convolutional Neural Networks
- 4. 《ImageNet Classification with Deep Convolutional Neural Network》
- 5. 轉：Awesome - Image Classification
- 6. Pre-training on Grayscale ImageNet Improves Medical Image Classification
- 7. 讀文獻——《ImageNet classification with deep convolutional neural networks》
- 8. ImageNet Classification with Deep Convolutional Neural Networks -- 解讀
- 9. AlexNet--ImageNet Classification with Deep Convolutional Neural Networks
- 10. 網絡壓縮論文集(network compression)