Unsupervised Hashing with Contrastive Information Bottleneck
This work improves unsupervised hashing for image retrieval by enhancing semantic preservation, though it is incremental as it adapts existing contrastive learning frameworks to hashing.
The paper tackled the problem of unsupervised hashing by addressing the issue of models focusing on reconstructing background information instead of discriminative semantic features, proposing a method based on contrastive learning and information bottleneck that significantly outperformed existing baselines on three benchmark image datasets.
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.