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.

本研究表明，在计算机视觉中，使用自监督学习获得的特征，在域泛化方面与或优于使用监督学习获得的特征，并引入了一个新的自监督预文本任务来预测对 Gabor 滤波器组的响应，使用多任务学习提高了域泛化性能。