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.

本文提出一种基于对比学习和信息瓶颈的二进制哈希方法，通过定义新的目标函数和引入概率二进制表示层来促进模型的端到端训练，证明了其与互信息的强联系，并在三个基准图像数据集上得到了显著优于现有基线的实验结果。

对比信息瓶颈下的无监督哈希算法

Unsupervised Hashing with Contrastive Information Bottleneck

With the increasing demand of massive multimodal data storage and
organization, cross-modal retrieval based on hashing technique has drawn much
attention nowadays. It takes the binary codes of one modality as the query to
retrieve the relevant hashing codes of another modality. However, the existing
binary constraint makes it difficult to find the optimal cross-modal hashing
function. Most approaches choose to relax the constraint and perform
thresholding strategy on the real-value representation instead of directly
solving the original objective. In this paper, we first provide a concrete
analysis about the effectiveness of multimodal networks in preserving the
inter- and intra-modal consistency. Based on the analysis, we provide a
so-called Deep Binary Reconstruction (DBRC) network that can directly learn the
binary hashing codes in an unsupervised fashion. The superiority comes from a
proposed simple but efficient activation function, named as Adaptive Tanh
(ATanh). The ATanh function can adaptively learn the binary codes and be
trained via back-propagation. Extensive experiments on three benchmark datasets
demonstrate that DBRC outperforms several state-of-the-art methods in both
image2text and text2image retrieval task.

本研究提出了一种基于深度学习的无监督二元建模网络，称为 Deep Binary Reconstruction（DBRC），采用 Adaptive Tanh（ATanh）作为激活函数，从而能够通过交叉模态检索任务学习到最佳的二元哈希码。实验结果表明，DBRC 在图像到文本和文本到图像的检索任务中优于其他先进方法。