Abstract: Owing to its storage and retrieval efficiency, hashing is widely applied to large-scale image retrieval. Most of existing deep hashing methods assume that the database in the target domain is identically distributed with the training set in the source domain. However, in practical applications, such assumption is so strict that there exists considerable domain discrepancy between source and target domain. To address such cross-domain image retrieval problem, some research works introduce domain adaptation techniques into image retrieval methods. The goal is to enhance the generalization ability of the learned hashing function. However, the learned Hash codes lack discrimination and domain-invariance in existing cross-domain hashing methods. We propose semantic preservation module and min-entropy loss to tackle these issues. We simply construct a classification sub-network as semantic preservation module to fully utilize labels in source domain. Semantic information encoded in labels can be passed to hashing learning network, which encourages learned Hash codes to contain more semantic information and discriminativity. As for unlabeled target domain samples, the entropy of their classification responses characterizes the confidence of classifier. Ideal target classification responses should tend to be one-hot vectors which minimizes the entropy. Therefore, we add minimization entropy loss to our model. Minimizing the entropy of classification responses of target samples aligns the distribution between source and target domain in classifier responses space. Therefore, the learned Hash codes tend to be more domain-invariant. With the semantic preservation module and min-entropy loss, we construct an end-to-end deep neural network for cross-domain image retrieval. Extensive experiments show the superiority of our model over existing state-of-the-art methods.