  Many unsupervised hashing methods are implicitly established on the idea of
reconstructing the input data, which basically encourages the hashing codes to
retain as much information of original data as possible. However, this
requirement may force the models spending lots of their effort on
reconstructing the unuseful background information, while ignoring to preserve
the discriminative semantic information that is more important for the hashing
task. To tackle this problem, inspired by the recent success of contrastive
learning in learning continuous representations, we propose to adapt this
framework to learn binary hashing codes. Specifically, we first propose to
modify the objective function to meet the specific requirement of hashing and
then introduce a probabilistic binary representation layer into the model to
facilitate end-to-end training of the entire model. We further prove the strong
connection between the proposed contrastive-learning-based hashing method and
the mutual information, and show that the proposed model can be considered
under the broader framework of the information bottleneck (IB). Under this
perspective, a more general hashing model is naturally obtained. Extensive
experimental results on three benchmark image datasets demonstrate that the
proposed hashing method significantly outperforms existing baselines.
