Generalizing to Unseen Domains via Adversarial Data Augmentation

NeurIPS 2018 Riccardo VolpiHongseok NamkoongOzan SenerJohn DuchiVittorio MurinoSilvio Savarese

We are concerned with learning models that generalize well to different \emph{unseen} domains. We consider a worst-case formulation over data distributions that are near the source domain in the feature space... (read more)

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