Binary vector embeddings enable fast nearest neighbor retrieval in large
databases of high-dimensional objects, and play an important role in many
practical applications, such as image and video retrieval. We study the problem
of learning binary vector embeddings under a supervised setting, also known as
hashing. We propose a novel supervised hashing method based on optimizing an
information-theoretic quantity: mutual information. We show that optimizing
mutual information can reduce ambiguity in the induced neighborhood structure
in the learned Hamming space, which is essential in obtaining high retrieval
performance. To this end, we optimize mutual information in deep neural
networks with minibatch stochastic gradient descent, with a formulation that
maximally and efficiently utilizes available supervision. Experiments on four
image retrieval benchmarks, including ImageNet, confirm the effectiveness of
our method in learning high-quality binary embeddings for nearest neighbor
retrieval.

该研究提出了一种基于互信息优化的新型监督式哈希方法，通过在深度神经网络中优化互信息来学习二进制向量嵌入，从而在大规模高维对象数据库检索中获得高质量的二进制嵌入。