  Self-supervised feature representations have been shown to be useful for
supervised classification, few-shot learning, and adversarial robustness. We
show that features obtained using self-supervised learning are comparable to,
or better than, supervised learning for domain generalization in computer
vision. We introduce a new self-supervised pretext task of predicting responses
to Gabor filter banks and demonstrate that multi-task learning of compatible
pretext tasks improves domain generalization performance as compared to
training individual tasks alone. Features learnt through self-supervision
obtain better generalization to unseen domains when compared to their
supervised counterpart when there is a larger domain shift between training and
test distributions and even show better localization ability for objects of
interest. Self-supervised feature representations can also be combined with
other domain generalization methods to further boost performance.
