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- Densely Connected Convolutional Networks - IEEE Xplore
In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion
- Densely Connected Convolutional Networks（稠密连接卷积网络）译文
Highway Networks [34]是第一批提供了一种有效训练具有100多个层的端到端网络的方法的架构。 使用旁路路径和选通单元，可以毫无困难地优化具有数百层的Highway Networks。 旁路路径被认为是简化这些深度网络训练的关键因素。
- Densely Connected Convolutional Networks - 百度学术
In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion Whereas traditional convolutional networks with L layers have L connections - one between each layer and its subsequent layer - our network has L (L+1) 2 direct connections
- Densely Connected Convolutional Networks - Computer
In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion
- [1608. 06993] Densely Connected Convolutional Networks
In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion
- Densely Connected Convolutional Networks - CVF Open Access
Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output
- Densely Connected Convolutional Networks (DenseNets) - GitHub
Introduction DenseNet is a network architecture where each layer is directly connected to every other layer in a feed-forward fashion (within each dense block) For each layer, the feature maps of all preceding layers are treated as separate inputs whereas its own feature maps are passed on as inputs to all subsequent layers
- Efficient densely connected convolutional neural networks
In this paper, we propose two efficient densely connected convolutional neural networks which are called DenseDsc and Dense2Net, respectively The two networks take advantage of the dense connection to improve feature reuse and use efficient convolutions for improving efficiency
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