  Hashing has been widely used in approximate nearest neighbor search for its
storage and computational efficiency. Deep supervised hashing methods are not
widely used because of the lack of labeled data, especially when the domain is
transferred. Meanwhile, unsupervised deep hashing models can hardly achieve
satisfactory performance due to the lack of reliable similarity signals. To
tackle this problem, we propose a novel deep unsupervised hashing method,
namely Distilled Smooth Guidance (DSG), which can learn a distilled dataset
consisting of similarity signals as well as smooth confidence signals. To be
specific, we obtain the similarity confidence weights based on the initial
noisy similarity signals learned from local structures and construct a priority
loss function for smooth similarity-preserving learning. Besides, global
information based on clustering is utilized to distill the image pairs by
removing contradictory similarity signals. Extensive experiments on three
widely used benchmark datasets show that the proposed DSG consistently
outperforms the state-of-the-art search methods.
