Unsupervised Binary Representation Learning with Deep Variational Networks

Learning to hash is regarded as an efficient approach for image retrieval and many other big-data applications. Recently, deep learning frameworks are adopted for image hashing, suggesting an alternative way to formulate the encoding function other than the conventional projections. Although deep learning has been proved to be successful in supervised hashing, existing unsupervised deep hashing techniques still cannot produce leading performance compared with the non-deep methods, as it is hard to unveil the intrinsic structure of the whole sample space by simply regularizing the output codes within each single training batch. To tackle this problem, in this paper, we propose a novel unsupervised deep hashing model, named deep variational binaries (DVB). The conditional auto-encoding variational Bayesian networks are introduced in this work to exploit the feature space structure of the training data using the latent variables. Integrating the probabilistic inference process with hashing objectives, the proposed DVB model estimates the statistics of data representations, and thus produces compact binary codes. Experimental results on three benchmark datasets, i.e., CIFAR-10, SUN-397 and NUS-WIDE, demonstrate that DVB outperforms state-of-the-art unsupervised hashing methods with significant margins.