Agnostic Domain Adaptation.

The supervised learning paradigm assumes in general that both training and test data are sampled from the same distribution. When this assumption is violated, we are in the setting of transfer learning or domain adaptation: Here, training data from a source domain, aim to learn a classifier which performs well on a target domain governed by a different distribution. We pursue an agnostic approach, assuming no information about the shift between source and target distributions but relying exclusively on unlabeled data from the target domain. Previous works [2] suggest that feature representations, which are invariant to domain change, increases generalization. Extending these ideas, we prove a generalization bound for domain adaptation that identifies the transfer mechanism: what matters is how much learnt classier itself is invariant, while feature representations may vary. Our bound is much tighter for rich hypothesis classes, which may only contain invariant classifier, but can not be invariant altogether. This concept is exemplified by the computer vision tasks of semantic segmentation and image categorization. Domain shift is simulated by introducing some common imaging distortions, such as gamma transform and color temperature shift. Our experiments on a public benchmark dataset confirm that using domain adapted classifier significantly improves accuracy when distribution changes are present.