  Nearest neighbor search aims to obtain the samples in the database with the
smallest distances from them to the queries, which is a basic task in a range
of fields, including computer vision and data mining. Hashing is one of the
most widely used methods for its computational and storage efficiency. With the
development of deep learning, deep hashing methods show more advantages than
traditional methods. In this survey, we detailedly investigate current deep
hashing algorithms including deep supervised hashing and deep unsupervised
hashing. Specifically, we categorize deep supervised hashing methods into
pairwise methods, ranking-based methods, pointwise methods as well as
quantization according to how measuring the similarities of the learned hash
codes. Moreover, deep unsupervised hashing is categorized into similarity
reconstruction-based methods, pseudo-label-based methods and prediction-free
self-supervised learning-based methods based on their semantic learning
manners. We also introduce three related important topics including
semi-supervised deep hashing, domain adaption deep hashing and multi-modal deep
hashing. Meanwhile, we present some commonly used public datasets and the
scheme to measure the performance of deep hashing algorithms. Finally, we
discuss some potential research directions in conclusion.
