  Deep neural networks have shown remarkable performance across a wide range of
vision-based tasks, particularly due to the availability of large-scale
datasets for training and better architectures. However, data seen in the real
world are often affected by distortions that not accounted for by the training
datasets. In this paper, we address the challenge of robustness and stability
of neural networks and propose a general training method that can be used to
make the existing neural network architectures more robust and stable to input
visual perturbations while using only available datasets for training. Proposed
training method is convenient to use as it does not require data augmentation
or changes in the network architecture. We provide theoretical proof as well as
empirical evidence for the efficiency of the proposed training method by
performing experiments with existing neural network architectures and
demonstrate that same architecture when trained with the proposed training
method perform better than when trained with conventional training approach in
the presence of noisy datasets.
