  Most recently Streaming Networks (STnets) have been introduced as a mechanism
of robust noise-corrupted images classification. STnets is a family of
convolutional neural networks, which consists of multiple neural networks
(streams), which have different inputs and their outputs are concatenated and
fed into a single joint classifier. The original paper has illustrated how
STnets can successfully classify images from Cifar10, EuroSat and UCmerced
datasets, when images were corrupted with various levels of random zero noise.
In this paper, we demonstrate that STnets are capable of high accuracy
classification of images corrupted with Gaussian noise, fog, snow, etc.
(Cifar10 corrupted dataset) and low light images (subset of Carvana dataset).
We also introduce a new type of STnets called Hybrid STnets. Thus, we
illustrate that STnets is a universal tool of image classification when
original training dataset is corrupted with noise or other transformations,
which lead to information loss from original images.
