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