Clustering and Separating Similarities for Deep Unsupervised Hashing

The lack of supervised information is the pivotal problem in unsupervised hashing. Most methods leverage deep features extracted from pre-trained models to generate semantic similarities as supervised information. These fixed features are, however, neither designed originally for retrieval nor updated adaptively during training. In this paper, we propose a novel deep Unsupervised Cluster and Separate Hashing (UCSH) to address these issues. Specifically, we introduce a fully end-to-end deep hashing network with a binary latent Variational AutoEncoder (VAE), which enables hash codes capable of reconstructing deep features as well as preserving semantic relations. Moreover, a 'Cluster and Separate' scheme is proposed to jointly cluster deep features and separate semantic similarities. Both the implicit feature clustering and the explicit similarity separating loss encourage the separation of similar and dissimilar pairs, enabling the iteratively updated similarities to better excavate semantic relations. Experiments conducted on three benchmarks show the superiority of UCSH.