Exemplar-based colorization aims to add colors to a grayscale image guided by a content related reference image. Existing methods are either sensitive to the selection of reference images (content, position) or extremely time and resource consuming, which limits their practical application. To tackle these problems, we propose a deep exemplar colorization architecture inspired by the characteristics of stylization in feature extracting and blending. Our coarse- to-fine architecture consists of two parts: a fast transfer sub-net and a robust colorization sub-net. The transfer sub- net obtains a coarse chrominance map via matching basic feature statistics of the input pairs in a progressive way. The colorization sub-net refines the map to generate the final results. The proposed end-to-end network can jointly learn faithful colorization with a related reference and plausible color prediction with unrelated reference. Extensive experimental validation demonstrates that our approach outperforms the state-of-the-art methods in less time whether in exemplar-based colorization or image stylization tasks.