The performance of any deep learning model depends heavily on the quantity and quality of the available training data. The generalization of the trained deep models improves with the availability of a large number of ...

Deep Neural Networks achieve near-human performance on several benchmark datasets, yet they are not as robust as humans. Their success relies on the proximity of test samples to the distribution of training data, resulting ...