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

Human brains are adept at dealing with the deluge of information they continuously receive, by suppressing the non-essential inputs and focusing on the important ones. Inspired by such capability, we propose Deluge Networks (DelugeNets), a novel class of neural networks facilitating massive cross-layer information inflows from preceding layers to succeeding layers. The connections between layers in DelugeNets are efficiently established through cross-layer depthwise convolutional layers with learnable filters, acting as a flexible selection mechanism. By virtue of the massive cross-layer information inflows, DelugeNets can propagate information across many layers with greater flexibility and utilize network parameters more effectively, compared to existing ResNet models. Experiments show the superior performances of DelugeNets in terms of both classification accuracies and parameter efficiencies. Remarkably, a DelugeNet model with just 20.2M parameters achieve state-ofthe-art error of 19.02% on CIFAR-100 dataset, outperforming DenseNet model with 27.2M parameters. Moreover, DelugeNet performs comparably to ResNet-200 on ImageNet dataset with merely half of the computations needed by the latter.