Weighted ChannelDropoutfor Regularization of Deep Convolutional Neural Network論文記錄
簡介 近年來，卷積神經網絡實現了很大的發展，這已經顯著提升了很多不同應用的性能表現，深度卷積神經網絡可以成功的原因很大程度是因爲其多個非線性隱藏層的結構，其中包含了數以百萬計的參數，因此可以學習輸入和輸出之間的關係。 作者觀察提出了一種新的方法，稱爲加權信道丟失率(WCD)的正則化深卷積神經網絡(CNN)。在一個卷積神經網絡的卷積堆棧內，所有的通道都是由之前的層生成的，並會在下一層得到平等的對待，
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相關文章
- 1. Deep Convolutional Neural Network Architecture
- 2. Improved Regularization of Convolutional Neural Networks with Cutout論文筆記
- 3. 【論文閱讀筆記】Imporved Regularization of Convolutional Neural Networks with Cutout
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