Abstract:Considerable progress has been made in domain generalization (DG) which aims to learn a generalizable model from multiple wellannotated source domains to unknown target domains. However, it can be prohibitively expensive to obtain sufficient annotation for source datasets in many real scenarios. To escape from the dilemma between domain generalization and annotation costs, in this paper, we introduce a novel task named label-efficient domain generalization (LEDG) to enable model generalization with label-limit… Show more
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