  Hashing is widely applied to large-scale image retrieval due to the storage
and retrieval efficiency. Existing work on deep hashing assumes that the
database in the target domain is identically distributed with the training set
in the source domain. This paper relaxes this assumption to a transfer
retrieval setting, which allows the database and the training set to come from
different but relevant domains. However, the transfer retrieval setting will
introduce two technical difficulties: first, the hash model trained on the
source domain cannot work well on the target domain due to the large
distribution gap; second, the domain gap makes it difficult to concentrate the
database points to be within a small Hamming ball. As a consequence, transfer
retrieval performance within Hamming Radius 2 degrades significantly in
existing hashing methods. This paper presents Transfer Adversarial Hashing
(TAH), a new hybrid deep architecture that incorporates a pairwise
$t$-distribution cross-entropy loss to learn concentrated hash codes and an
adversarial network to align the data distributions between the source and
target domains. TAH can generate compact transfer hash codes for efficient
image retrieval on both source and target domains. Comprehensive experiments
validate that TAH yields state of the art Hamming space retrieval performance
on standard datasets.
