  A deep hashing model typically has two main learning objectives: to make the
learned binary hash codes discriminative and to minimize a quantization error.
With further constraints such as bit balance and code orthogonality, it is not
uncommon for existing models to employ a large number (>4) of losses. This
leads to difficulties in model training and subsequently impedes their
effectiveness. In this work, we propose a novel deep hashing model with only a
single learning objective. Specifically, we show that maximizing the cosine
similarity between the continuous codes and their corresponding binary
orthogonal codes can ensure both hash code discriminativeness and quantization
error minimization. Further, with this learning objective, code balancing can
be achieved by simply using a Batch Normalization (BN) layer and multi-label
classification is also straightforward with label smoothing. The result is an
one-loss deep hashing model that removes all the hassles of tuning the weights
of various losses. Importantly, extensive experiments show that our model is
highly effective, outperforming the state-of-the-art multi-loss hashing models
on three large-scale instance retrieval benchmarks, often by significant
margins. Code is available at https://github.com/kamwoh/orthohash
