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We introduce a novel unsupervised domain adaptation approach for objectdetection. We aim to alleviate the imperfect translation problem of pixel-leveladaptations, and the source-biased discriminativity problem of feature-leveladaptations simultaneously. Our approach is composed of two stages, i.e.,Domain Diversification (DD) and Multi-domain-invariant Representation Learning(MRL). At the DD stage, we diversify the distribution of the labeled data bygenerating various distinctive shifted domains from the source domain. At theMRL stage, we apply adversarial learning with a multi-domain discriminator toencourage feature to be indistinguishable among the domains. DD addresses thesource-biased discriminativity, while MRL mitigates the imperfect imagetranslation. We construct a structured domain adaptation framework for ourlearning paradigm and introduce a practical way of DD for implementation. Ourmethod outperforms the state-of-the-art methods by a large margin of 3%~11% interms of mean average precision (mAP) on various datasets.

Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection