  Fast similarity search is a key component in large-scale information
retrieval, where semantic hashing has become a popular strategy for
representing documents as binary hash codes. Recent advances in this area have
been obtained through neural network based models: generative models trained by
learning to reconstruct the original documents. We present a novel unsupervised
generative semantic hashing approach, \textit{Ranking based Semantic Hashing}
(RBSH) that consists of both a variational and a ranking based component.
Similarly to variational autoencoders, the variational component is trained to
reconstruct the original document conditioned on its generated hash code, and
as in prior work, it only considers documents individually. The ranking
component solves this limitation by incorporating inter-document similarity
into the hash code generation, modelling document ranking through a hinge loss.
To circumvent the need for labelled data to compute the hinge loss, we use a
weak labeller and thus keep the approach fully unsupervised.
  Extensive experimental evaluation on four publicly available datasets against
traditional baselines and recent state-of-the-art methods for semantic hashing
shows that RBSH significantly outperforms all other methods across all
evaluated hash code lengths. In fact, RBSH hash codes are able to perform
similarly to state-of-the-art hash codes while using 2-4x fewer bits.
