Wide Residual Network (WRN)

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A Wide Residual Network (WRN) is a Deep Residual Neural Network that contains wide-dropout residual blocks.



References

2016

[math]\displaystyle{ \mathbf{x}_{l+1}=\mathbf{x}_{l}+\mathcal{F}\left(\mathbf{x}_{l}, \mathcal{W}_{l}\right) }[/math] (1)
where $\mathbf{x}_{l+1}$ and $\mathbf{x}_{l}$ are input and output of the $l$-th unit in the network, $\mathcal{F}$ is a residual function and $\mathcal{W}_{l}$ are parameters of the block. Residual network consists of sequentially stacked residual block.

2016 WideResidualNetwork Fig1.png
Figure 1: Various residual blocks used in the paper. Batch normalization and ReLU precede each convolution (omitted for clarity).