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Generalizing to Unseen Domains via Adversarial Data Augmentation
Riccardo Volpi · Hongseok Namkoong · Ozan Sener · John Duchi · Vittorio Murino · Silvio Savarese

Wed Dec 05 07:45 AM -- 09:45 AM (PST) @ Room 210 #42

We are concerned with learning models that generalize well to different unseen domains. We consider a worst-case formulation over data distributions that are near the source domain in the feature space. Only using training data from a single source distribution, we propose an iterative procedure that augments the dataset with examples from a fictitious target domain that is "hard" under the current model. We show that our iterative scheme is an adaptive data augmentation method where we append adversarial examples at each iteration. For softmax losses, we show that our method is a data-dependent regularization scheme that behaves differently from classical regularizers that regularize towards zero (e.g., ridge or lasso). On digit recognition and semantic segmentation tasks, our method learns models improve performance across a range of a priori unknown target domains.

Author Information

Riccardo Volpi (Istituto Italiano di Tecnologia)
Hongseok Namkoong (Stanford University)
Ozan Sener (Intel Labs)
John Duchi (Stanford)
Vittorio Murino (Istituto Italiano di Tecnologia)
Silvio Savarese (Stanford University)

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