  Deep representation learning has become one of the most widely adopted
approaches for visual search, recommendation, and identification. Retrieval of
such representations from a large database is however computationally
challenging. Approximate methods based on learning compact representations,
have been widely explored for this problem, such as locality sensitive hashing,
product quantization, and PCA. In this work, in contrast to learning compact
representations, we propose to learn high dimensional and sparse
representations that have similar representational capacity as dense embeddings
while being more efficient due to sparse matrix multiplication operations which
can be much faster than dense multiplication. Following the key insight that
the number of operations decreases quadratically with the sparsity of
embeddings provided the non-zero entries are distributed uniformly across
dimensions, we propose a novel approach to learn such distributed sparse
embeddings via the use of a carefully constructed regularization function that
directly minimizes a continuous relaxation of the number of floating-point
operations (FLOPs) incurred during retrieval. Our experiments show that our
approach is competitive to the other baselines and yields a similar or better
speed-vs-accuracy tradeoff on practical datasets.
