  We introduce a novel unsupervised domain adaptation approach for object
detection. We aim to alleviate the imperfect translation problem of pixel-level
adaptations, and the source-biased discriminativity problem of feature-level
adaptations 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 by
generating various distinctive shifted domains from the source domain. At the
MRL stage, we apply adversarial learning with a multi-domain discriminator to
encourage feature to be indistinguishable among the domains. DD addresses the
source-biased discriminativity, while MRL mitigates the imperfect image
translation. We construct a structured domain adaptation framework for our
learning paradigm and introduce a practical way of DD for implementation. Our
method outperforms the state-of-the-art methods by a large margin of 3%~11% in
terms of mean average precision (mAP) on various datasets.
