  Hashing method maps similar high-dimensional data to binary hashcodes with
smaller hamming distance, and it has received broad attention due to its low
storage cost and fast retrieval speed. Pairwise similarity is easily obtained
and widely used for retrieval, and most supervised hashing algorithms are
carefully designed for the pairwise supervisions. As labeling all data pairs is
difficult, semi-supervised hashing is proposed which aims at learning efficient
codes with limited labeled pairs and abundant unlabeled ones. Existing methods
build graphs to capture the structure of dataset, but they are not working well
for complex data as the graph is built based on the data representations and
determining the representations of complex data is difficult. In this paper, we
propose a novel teacher-student semi-supervised hashing framework in which the
student is trained with the pairwise information produced by the teacher
network. The network follows the smoothness assumption, which achieves
consistent distances for similar data pairs so that the retrieval results are
similar for neighborhood queries. Experiments on large-scale datasets show that
the proposed method reaches impressive gain over the supervised baselines and
is superior to state-of-the-art semi-supervised hashing methods.
