  While existing work in robust deep learning has focused on small pixel-level
norm-based perturbations, this may not account for perturbations encountered in
several real-world settings. In many such cases although test data might not be
available, broad specifications about the types of perturbations (such as an
unknown degree of rotation) may be known. We consider a setup where robustness
is expected over an unseen test domain that is not i.i.d. but deviates from the
training domain. While this deviation may not be exactly known, its broad
characterization is specified a priori, in terms of attributes. We propose an
adversarial training approach which learns to generate new samples so as to
maximize exposure of the classifier to the attributes-space, without having
access to the data from the test domain. Our adversarial training solves a
min-max optimization problem, with the inner maximization generating
adversarial perturbations, and the outer minimization finding model parameters
by optimizing the loss on adversarial perturbations generated from the inner
maximization. We demonstrate the applicability of our approach on three types
of naturally occurring perturbations -- object-related shifts, geometric
transformations, and common image corruptions. Our approach enables deep neural
networks to be robust against a wide range of naturally occurring
perturbations. We demonstrate the usefulness of the proposed approach by
showing the robustness gains of deep neural networks trained using our
adversarial training on MNIST, CIFAR-10, and a new variant of the CLEVR
dataset.
