  Deep Neural Networks, often owing to the overparameterization, are shown to
be capable of exactly memorizing even randomly labelled data. Empirical studies
have also shown that none of the standard regularization techniques mitigate
such overfitting. We investigate whether the choice of the loss function can
affect this memorization. We empirically show, with benchmark data sets MNIST
and CIFAR-10, that a symmetric loss function, as opposed to either
cross-entropy or squared error loss, results in significant improvement in the
ability of the network to resist such overfitting. We then provide a formal
definition for robustness to memorization and provide a theoretical explanation
as to why the symmetric losses provide this robustness. Our results clearly
bring out the role loss functions alone can play in this phenomenon of
memorization.
