Convolutional Neural Networks with Alternately Updated Clique
摘要 改善深层网络中的信息流有助于缓解训练难度并更有效地利用参数。这篇文章提出了一个新的卷积神经网络架构与交替更新的派系（CliqueNet）。同一块中的任何两层之间都有前向和后向连接，这些图层被构造为一个循环并交替更新。 CliqueNet有一些独特的属性：对于每一层来说，它既是同一块中任何其他层的输入和输出，也是层间信息流最大化的结果。在传播过程中，新更新的图层被连接起来以重新
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相关文章
- 1. Convolutional Neural Networks with Alternately Updated Clique
- 2. CliqueNet:Convolutional Neural Networks with Alternately Updated Clique详解
- 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