Deep Unsupervised Hashing by Distilled Smooth Guidance

Hashing has been widely used in approximate nearest neighbor search recently. 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. Here, 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. Specifically, 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 bench-mark datasets show that the proposed DSG consistently out-performs the state-of-the-art search methods.