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Deluge Networks (DelugeNets) are deep neural networks which efficientlyfacilitate massive cross-layer information inflows from preceding layers tosucceeding layers. The connections between layers in DelugeNets are establishedthrough cross-layer depthwise convolutional layers with learnable filters,acting as a flexible yet efficient selection mechanism. DelugeNets canpropagate information across many layers with greater flexibility and utilizenetwork parameters more effectively compared to ResNets, whilst being moreefficient than DenseNets. Remarkably, a DelugeNet model with just modelcomplexity of 4.31 GigaFLOPs and 20.2M network parameters, achieveclassification errors of 3.76% and 19.02% on CIFAR-10 and CIFAR-100 datasetrespectively. Moreover, DelugeNet-122 performs competitively to ResNet-200 onImageNet dataset, despite costing merely half of the computations needed by thelatter.

DelugeNets: Deep Networks with Efficient and Flexible Cross-layer Information Inflows