  Deep Learning has become interestingly popular in computer vision, mostly
attaining near or above human-level performance in various vision tasks. But
recent work has also demonstrated that these deep neural networks are very
vulnerable to adversarial examples (adversarial examples - inputs to a model
which are naturally similar to original data but fools the model in classifying
it into a wrong class). Humans are very robust against such perturbations; one
possible reason could be that humans do not learn to classify based on an error
between "target label" and "predicted label" but possibly due to reinforcements
that they receive on their predictions. In this work, we proposed a novel
method to train deep learning models on an image classification task. We used a
reward-based optimization function, similar to the vanilla policy gradient
method used in reinforcement learning, to train our model instead of
conventional cross-entropy loss. An empirical evaluation on the cifar10 dataset
showed that our method learns a more robust classifier than the same model
architecture trained using cross-entropy loss function (on adversarial
training). At the same time, our method shows a better generalization with the
difference in test accuracy and train accuracy $< 2\%$ for most of the time
compared to the cross-entropy one, whose difference most of the time remains $>
2\%$.
