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

Embarrassingly Simple Binary Representation Learning