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Due to the high storage and search efficiency, hashing has become prevalentfor large-scale similarity search. Particularly, deep hashing methods havegreatly improved the search performance under supervised scenarios. Incontrast, unsupervised deep hashing models can hardly achieve satisfactoryperformance due to the lack of reliable supervisory similarity signals. Toaddress this issue, we propose a novel deep unsupervised hashing model, dubbedDistillHash, which can learn a distilled data set consisted of data pairs,which have confidence similarity signals. Specifically, we investigate therelationship between the initial noisy similarity signals learned from localstructures and the semantic similarity labels assigned by a Bayes optimalclassifier. We show that under a mild assumption, some data pairs, of whichlabels are consistent with those assigned by the Bayes optimal classifier, canbe potentially distilled. Inspired by this fact, we design a simple yeteffective strategy to distill data pairs automatically and further adopt aBayesian learning framework to learn hash functions from the distilled dataset. Extensive experimental results on three widely used benchmark datasetsshow that the proposed DistillHash consistently accomplishes thestate-of-the-art search performance.

DistillHash: Unsupervised Deep Hashing by Distilling Data Pairs