  Hashing produces compact representations for documents, to perform tasks like
classification or retrieval based on these short codes. When hashing is
supervised, the codes are trained using labels on the training data. This paper
first shows that the evaluation protocols used in the literature for supervised
hashing are not satisfactory: we show that a trivial solution that encodes the
output of a classifier significantly outperforms existing supervised or
semi-supervised methods, while using much shorter codes. We then propose two
alternative protocols for supervised hashing: one based on retrieval on a
disjoint set of classes, and another based on transfer learning to new classes.
We provide two baseline methods for image-related tasks to assess the
performance of (semi-)supervised hashing: without coding and with unsupervised
codes. These baselines give a lower- and upper-bound on the performance of a
supervised hashing scheme.
