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Improving out-of-distribution generalization via multi-task self-supervised pretraining

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arxiv 2003.13525 v1 pith:7JBGJEJG submitted 2020-03-30 cs.CV cs.LG

Isabela Albuquerque , Nikhil Naik , Junnan Li , Nitish Keskar , Richard Socher This is my paper · ORCID
classification cs.CVcs.LG
keywords generalizationself-superviseddomainlearningbettersupervisedcomparedfeature
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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.

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Cited by 5 Pith papers

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