Convolutional Neural Networks with Alternately Updated Clique
摘要 改善深層網絡中的信息流有助於緩解訓練難度並更有效地利用參數。這篇文章提出了一個新的卷積神經網絡架構與交替更新的派系（CliqueNet）。同一塊中的任何兩層之間都有前向和後向連接，這些圖層被構造爲一個循環並交替更新。 CliqueNet有一些獨特的屬性：對於每一層來說，它既是同一塊中任何其他層的輸入和輸出，也是層間信息流最大化的結果。在傳播過程中，新更新的圖層被連接起來以重新
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相關文章
- 1. Convolutional Neural Networks with Alternately Updated Clique
- 2. CliqueNet:Convolutional Neural Networks with Alternately Updated Clique詳解
- 3. Article Analysis (AA): Alternately Updated Clique
- 4. ImageNet Classification with Deep Convolutional Neural Networks
- 5. AlexNet：ImageNet Classification with Deep Convolutional Neural Networks
- 6. ImageNet Classification with Deep Convolutional Neural Networks
- 7. IMAGE DENOISING WITH GRAPH-CONVOLUTIONAL NEURAL NETWORKS
- 8. Convolutional Neural Networks: Application
- 9. Convolutional Neural Networks---Foundations of Convolutional Neural Networks
- 10. C4:Convolutional Neural Networks