  Recent binary representation learning models usually require sophisticated
binary optimization, similarity measure or even generative models as
auxiliaries. However, one may wonder whether these non-trivial components are
needed to formulate practical and effective hashing models. In this paper, we
answer the above question by proposing an embarrassingly simple approach to
binary representation learning. With a simple classification objective, our
model only incorporates two additional fully-connected layers onto the top of
an arbitrary backbone network, whilst complying with the binary constraints
during training. The proposed model lower-bounds the Information Bottleneck
(IB) between data samples and their semantics, and can be related to many
recent `learning to hash' paradigms. We show that, when properly designed, even
such a simple network can generate effective binary codes, by fully exploring
data semantics without any held-out alternating updating steps or auxiliary
models. Experiments are conducted on conventional large-scale benchmarks, i.e.,
CIFAR-10, NUS-WIDE, and ImageNet, where the proposed simple model outperforms
the state-of-the-art methods.
