This paper is different from most works focus on reducing the domain shift from perspective of loss functions, contributing to network design by developing a novel transferable normalization (TranNorm) layer. TranNorm is well motivated, separately normalizing source and target features in a minibatch and meanwhile weighting each channel in terms of transferability. It is clear different from and meanwhile significantly outperformes related methods, e.g., AdaBN [15] and AutoDIAL [21]. The TranNorm layer is simple and free of parameters, which can be conveniently plugged in mainstream networks. I think that this work will have a non-trivial impact: the proposed TranNorm can be used as backbone layer improving other state-of-the-art methods.