  Deep neural networks (DNNs) provide high image classification accuracy, but
experience significant performance degradation when perturbation from various
sources are present in the input. The lack of resilience to input perturbations
makes DNN less reliable for systems interacting with physical world such as
autonomous vehicles, robotics, to name a few, where imperfect input is the
normal condition. We present a hybrid deep network architecture with
spike-assisted contextual information extraction (ScieNet). ScieNet integrates
unsupervised learning using spiking neural network (SNN) for unsupervised
contextual informationextraction with a back-end DNN trained for
classification. The integrated network demonstrates high resilience to input
perturbations without relying on prior training on perturbed inputs. We
demonstrate ScieNet with different back-end DNNs for image classification using
CIFAR dataset considering stochastic (noise) and structured (rain) input
perturbations. Experimental results demonstrate significant improvement in
accuracy on noisy and rainy images without prior training, while maintaining
state-of-the-art accuracy on clean images.
