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文件名称：深度学习参考文献
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更新时间：2021-06-16 07:50:31
深度学习文献
Improving information flow in deep networks helps to
ease the training difficulties and utilize parameters more
efficiently. Here we propose a new convolutional neural
network architecture with alternately updated clique
(CliqueNet). In contrast to prior networks, there are both
forward and backward connections between any two layers
in the same block. The layers are constructed as a loop and
are updated alternately. The CliqueNet has some unique
properties. For each layer, it is both the input and output of
any other layer in the same block, so that the information
flow among layers is maximized.