  Hashing techniques are in great demand for a wide range of real-world
applications such as image retrieval and network compression. Nevertheless,
existing approaches could hardly guarantee a satisfactory performance with the
extremely low-bit (e.g., 4-bit) hash codes due to the severe information loss
and the shrink of the discrete solution space. In this paper, we propose a
novel \textit{Collaborative Learning} strategy that is tailored for generating
high-quality low-bit hash codes. The core idea is to jointly distill
bit-specific and informative representations for a group of pre-defined code
lengths. The learning of short hash codes among the group can benefit from the
manifold shared with other long codes, where multiple views from different hash
codes provide the supplementary guidance and regularization, making the
convergence faster and more stable. To achieve that, an asymmetric hashing
framework with two variants of multi-head embedding structures is derived,
termed as Multi-head Asymmetric Hashing (MAH), leading to great efficiency of
training and querying. Extensive experiments on three benchmark datasets have
been conducted to verify the superiority of the proposed MAH, and have shown
that the 8-bit hash codes generated by MAH achieve $94.3\%$ of the MAP (Mean
Average Precision (MAP)) score on the CIFAR-10 dataset, which significantly
surpasses the performance of the 48-bit codes by the state-of-the-arts in image
retrieval tasks.
