DeepGCNs- Can GCNs Go as Deep as CNNs?
本篇文章主要闡述了我們怎麼去構建一個網絡使得GCN能夠堆疊更深的層且不會發生梯度消失的問題。講了3個method：1.Resnet 2.Densenet 3.Dilated convolutions 。 該模型的很多技巧以及method對其他的方向感覺有比較大的借鑑意義。 Introduction 在目前我們所用到的GCN中，一般layers都大致在4層左右，並不會堆疊更多，主要是因爲隨着層數的增
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相關文章
- 1. DeepGCNs-Can GCNs Go as Deep as CNNs? ICCV 2019
- 2. DeepGCNs: Can GCNs Go as Deep as CNNs? 論文解析
- 3. 論文筆記：DeepGCNs: Can GCNs Go as Deep as CNNs?
- 4. ICCV19開源論文 DeepGCNs: Can GCNs Go as Deep as CNNs?
- 5. 小白科研筆記：簡析論文DeepGCNs: Can GCNs Go as Deep as CNNs
- 6. [論文筆記]：ResGCN-Can GCNs Go as Deep as CNNs?
- 7. ICCV2019點雲論文 ResGCN-Can GCNs Go as Deep as CNNs
- 8. Training RNNs as Fast as CNNs
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