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The CNNs have achieved a state-of-the-art performance in many applications.Recent studies illustrate that CNN's recognition accuracy drops drastically ifimages are noise corrupted. We focus on the problem of robust recognitionaccuracy of noise-corrupted images. We introduce a novel network architecturecalled Streaming Networks. Each stream is taking a certain intensity slice ofthe original image as an input, and stream parameters are trainedindependently. We use network capacity, hard-wired and input-induced sparsityas the dimensions for experiments. The results indicate that only the presenceof both hard-wired and input-induces sparsity enables robust noisy imagerecognition. Streaming Nets is the only architecture which has both types ofsparsity and exhibits higher robustness to noise. Finally, to illustrateincrease in filter diversity we illustrate that a distribution of filterweights of the first conv layer gradually approaches uniform distribution asthe degree of hard-wired and domain-induced sparsity and capacities increases.

Streaming Networks: Increase Noise Robustness and Filter Diversity via Hard-wired and Input-induced Sparsity