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Deep Residual Networks have reached the state of the art in many imageprocessing tasks such image classification. However, the cost for a gain inaccuracy in terms of depth and memory is prohibitive as it requires a highernumber of residual blocks, up to double the initial value. To tackle thisproblem, we propose in this paper a way to reduce the redundant information ofthe networks. We share the weights of convolutional layers between residualblocks operating at the same spatial scale. The signal flows multiple times inthe same convolutional layer. The resulting architecture, called ShaResNet,contains block specific layers and shared layers. These ShaResNet are trainedexactly in the same fashion as the commonly used residual networks. We show, onthe one hand, that they are almost as efficient as their sequentialcounterparts while involving less parameters, and on the other hand that theyare more efficient than a residual network with the same number of parameters.For example, a 152-layer-deep residual network can be reduced to 106convolutional layers, i.e. a parameter gain of 39\%, while loosing less than0.2\% accuracy on ImageNet.

ShaResNet: reducing residual network parameter number by sharing weights