  Conventional unsupervised hashing methods usually take advantage of
similarity graphs, which are either pre-computed in the high-dimensional space
or obtained from random anchor points. On the one hand, existing methods
uncouple the procedures of hash function learning and graph construction. On
the other hand, graphs empirically built upon original data could introduce
biased prior knowledge of data relevance, leading to sub-optimal retrieval
performance. In this paper, we tackle the above problems by proposing an
efficient and adaptive code-driven graph, which is updated by decoding in the
context of an auto-encoder. Specifically, we introduce into our framework twin
bottlenecks (i.e., latent variables) that exchange crucial information
collaboratively. One bottleneck (i.e., binary codes) conveys the high-level
intrinsic data structure captured by the code-driven graph to the other (i.e.,
continuous variables for low-level detail information), which in turn
propagates the updated network feedback for the encoder to learn more
discriminative binary codes. The auto-encoding learning objective literally
rewards the code-driven graph to learn an optimal encoder. Moreover, the
proposed model can be simply optimized by gradient descent without violating
the binary constraints. Experiments on benchmarked datasets clearly show the
superiority of our framework over the state-of-the-art hashing methods. Our
source code can be found at https://github.com/ymcidence/TBH.
