  Generalization is the ability of a model to predict on unseen domains and is
a fundamental task in machine learning. Several generalization bounds, both
theoretical and empirical have been proposed but they do not provide tight
bounds .In this work, we propose a simple yet effective method to predict the
generalization performance of a model by using the concept that models that are
robust to augmentations are more generalizable than those which are not. We
experiment with several augmentations and composition of augmentations to check
the generalization capacity of a model. We also provide a detailed motivation
behind the proposed method. The proposed generalization metric is calculated
based on the change in the output of the model after augmenting the input. The
proposed method was the first runner up solution for the NeurIPS competition on
Predicting Generalization in Deep Learning.
