【泛讀】NIPS- Break the Ceiling:Stronger Multi-scale Deep GCNs
Break the Ceiling:Stronger Multi-scale Deep GCNs Whats the problem? 現存的圖神經網絡沒有充分利用多尺度信息和深層架構。 1.如果基於GCN架構增加層數，提取的特徵最多保留圖結構的固定信息，並且丟失節點特徵中的所有信息。 2.ReLU是一個陰謀家。 代碼鏈接 How to solve them? 作者證明了任何具有明確定義的解析光譜
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相關文章
- 1. DeepGCNs- Can GCNs Go as Deep as CNNs?
- 2. Visual saliency detection based on multiscale deep cnn features
- 3. DeepGCNs-Can GCNs Go as Deep as CNNs? ICCV 2019
- 4. nips 208 visual question answering 導讀
- 5. NIPS-2013：Playing Atari with Deep Reinforcement Learning
- 6. Paper-1 精讀GAIL 2016 NIPS
- 7. 2017-04-18 Visual Saliency Detection Based on Multiscale Deep CNN Features
- 8. [NIPS 2018] Generalized Zero-Shot Learning with Deep Calibration Network
- 9. Paper-2 精讀GPS (2014 NIPS)
- 10. ICCV2019點雲論文 ResGCN-Can GCNs Go as Deep as CNNs