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Recently, Neural networks have seen a huge surge in its adoption due to theirability to provide high accuracy on various tasks. On the other hand, theexistence of adversarial examples have raised suspicions regarding thegeneralization capabilities of neural networks. In this work, we focus on theweight matrix learnt by the neural networks and hypothesize that illconditioned weight matrix is one of the contributing factors in neuralnetwork's susceptibility towards adversarial examples. For ensuring that thelearnt weight matrix's condition number remains sufficiently low, we suggestusing orthogonal regularizer. We show that this indeed helps in increasing theadversarial accuracy on MNIST and F-MNIST datasets.

Neural Networks in Adversarial Setting and Ill-Conditioned Weight Space